Data Scientist Professional Practical Exam Submission¶
Use this template to write up your summary for submission. Code in Python or R needs to be included.
📝 Task List¶
Your written report should include both code, output and written text summaries of the following:
- Data Validation:
- Describe validation and cleaning steps for every column in the data
- Exploratory Analysis:
- Include two different graphics showing single variables only to demonstrate the characteristics of data
- Include at least one graphic showing two or more variables to represent the relationship between features
- Describe your findings
- Model Development
- Include your reasons for selecting the models you use as well as a statement of the problem type
- Code to fit the baseline and comparison models
- Model Evaluation
- Describe the performance of the two models based on an appropriate metric
- Business Metrics
- Define a way to compare your model performance to the business
- Describe how your models perform using this approach
- Final summary including recommendations that the business should undertake
Start writing report here..
1 | Importing Libraries & Data ¶
# Data Manipulation and Analysis
import pandas as pd # Data manipulation and analysis library
# Warnings and Errors
import warnings # Library to ignore warnings in the code
warnings.filterwarnings('ignore') # Ignore warnings in the code
# Data Visualization
import matplotlib.pyplot as plt # Data visualization library
import seaborn as sns # Statistical data visualization library
# Statistical Modeling and Computing
import statsmodels.api as sm # Statistical modeling library
import scipy.stats as stats # Scientific computing library for statistical functions
from scipy.stats import boxcox, skew # Statistical functions for data transformation
# Machine Learning
from sklearn.tree import DecisionTreeClassifier # Decision Tree Classifier for classification tasks
from sklearn.ensemble import RandomForestClassifier # Random Forest Classifier for classification tasks
from sklearn.ensemble import IsolationForest # Anomaly detection algorithm
# Data Preprocessing
from sklearn.preprocessing import MinMaxScaler # Scaling/normalizing data
from sklearn.preprocessing import StandardScaler # Scaling/normalizing data
from sklearn.preprocessing import LabelEncoder # Encoding categorical variables
from sklearn.impute import SimpleImputer # Handling missing values
# Model Selection and Evaluation
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score # Model selection and evaluation
from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix # Evaluation metrics for classification models
import flask # Web Page Development
import joblib # Library for saving and loading Python objects
df = pd.read_csv('recipe_site_traffic_2212.csv')
df.head()
| recipe | calories | carbohydrate | sugar | protein | category | servings | high_traffic | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | NaN | NaN | NaN | NaN | Pork | 6 | High |
| 1 | 2 | 35.48 | 38.56 | 0.66 | 0.92 | Potato | 4 | High |
| 2 | 3 | 914.28 | 42.68 | 3.09 | 2.88 | Breakfast | 1 | NaN |
| 3 | 4 | 97.03 | 30.56 | 38.63 | 0.02 | Beverages | 4 | High |
| 4 | 5 | 27.05 | 1.85 | 0.80 | 0.53 | Beverages | 4 | NaN |
2 | Understanding Our Data (EDA)¶
2.1 | Shape¶
#What is the shape of the dataset?
df.shape
(947, 8)
2.2 | Statistical Summary of Dataset¶
# Generate summary statistics for numerical columns
summary_stats = df.describe(include='number')
# Function to style the DataFrame with a background color
def highlight_background(val):
"""Apply background color and text color to all cells."""
return 'background-color: #eac086; color: black'
# Function to style the column headers
def apply_styles():
"""Apply consistent background color for headers."""
styles = {
'selector': 'th',
'props': [('background-color', '#eac086'), ('color', 'black')]
}
return [styles]
# Apply the styling to the summary statistics DataFrame
styled_summary = summary_stats.style.applymap(highlight_background)\
.set_table_styles(apply_styles())\
.set_caption("Summary Statistics for Numerical Columns")
# Display the styled DataFrame
styled_summary
| recipe | calories | carbohydrate | sugar | protein | servings | |
|---|---|---|---|---|---|---|
| count | 574.000000 | 574.000000 | 574.000000 | 574.000000 | 574.000000 | 574.000000 |
| mean | 480.205575 | 451.673920 | 36.864286 | 7.893850 | 24.481098 | 3.545296 |
| std | 272.564979 | 456.225578 | 47.418157 | 12.645207 | 36.799484 | 1.744761 |
| min | 1.000000 | 0.140000 | 0.030000 | 0.010000 | 0.000000 | 1.000000 |
| 25% | 245.250000 | 128.482500 | 9.645000 | 1.730000 | 4.952500 | 2.000000 |
| 50% | 473.500000 | 288.550000 | 21.480000 | 4.550000 | 10.800000 | 4.000000 |
| 75% | 719.750000 | 607.422500 | 45.292500 | 8.147500 | 28.625000 | 4.000000 |
| max | 946.000000 | 2906.010000 | 530.420000 | 131.390000 | 363.360000 | 6.000000 |
Summary Statistics Insights¶
Recipe Column¶
- Mean: 480.21
- Range: 1 to 946
- Interquartile Range (IQR): 474.50
Calories¶
- Mean: 451.67
- Range: 0.14 to 2906.01
- IQR: 478.94
- Insight: This column shows a wide variability in calorie content, highlighting a broad range of recipe types from low-calorie to high-calorie.
Carbohydrate¶
- Mean: 36.86
- Range: 0.03 to 530.42
- IQR: 35.64
- Insight: Significant variation in carbohydrate content, reflecting a diverse array of recipes.
Sugar¶
- Mean: 7.89
- Range: 0.01 to 131.39
- IQR: 6.42
- Insight: Sugar content varies considerably, indicating recipes with both low and high sugar levels.
Protein¶
- Mean: 24.48
- Range: 0.00 to 363.36
- IQR: 23.67
- Insight: A broad range in protein content, suggesting recipes with varying protein levels from minimal to high.
Servings¶
- Mean: 3.55
- Range: 1 to 6
- IQR: 2
- Insight: Most recipes are designed to serve between 2 and 6 people, with a few exceptions.
2.3 | Information Dataset¶
# Extract information about DataFrame
df_info = pd.DataFrame({
'Non-Null Count': df.notnull().sum(),
'Data Type': df.dtypes
})
# Apply stylish formatting with custom colors
styled_df_info = (
df_info.style
.set_properties(**{
'background-color': '#F0F8FF', # Background color for the entire table
'color': 'black', # Text color
'border': '1px solid black', # Border color
'padding': '8px' # Padding for cells
})
.set_caption('DataFrame Information: Attributes and Data Types') # Add a title to the table
.set_table_styles([
{'selector': 'th', 'props': [('background-color', '#F0F8FF')]}, # Bad heading background color
])
)
# Display the styled DataFrame
styled_df_info
| Non-Null Count | Data Type | |
|---|---|---|
| recipe | 947 | int64 |
| calories | 895 | float64 |
| carbohydrate | 895 | float64 |
| sugar | 895 | float64 |
| protein | 895 | float64 |
| category | 947 | object |
| servings | 947 | object |
| high_traffic | 574 | object |
2.4 | Null Values Handling¶
# Calculate the number of null values and their percentages
null_counts = df.isnull().sum()
total_rows = len(df)
null_percentages = (null_counts / total_rows) * 100
# Create a DataFrame to display the counts and percentages
null_summary = pd.DataFrame({
'Null Values': null_counts,
'Percentage': null_percentages
})
# Apply stylish formatting with custom colors
styled_null_summary = (
null_summary.style
.format({'Percentage': '{:.2f}%'}) # Format percentage to two decimal places
.background_gradient(cmap='coolwarm', subset=['Percentage']) # Apply a gradient to the 'Percentage' column
.highlight_max(subset=['Null Values'], color='lightcoral') # Highlight the row with the maximum null values
.set_caption('Summary of Null Values and Their Percentages') # Add a title to the table
.set_table_styles([
{'selector': 'thead th', 'props': [('background-color', '#FFE4E1'), # Header background color
('color', 'black'), # Header text color
('font-weight', 'bold')]},
{'selector': 'tbody tr:hover', 'props': [('background-color', '#FFE4E1')]}, # Hover effect with background color
{'selector': 'tbody td', 'props': [('background-color', '#FFE4E1'), # Table body background color
('color', 'black'), # Table body text color
('border', '1px solid black'), # Border color
('padding', '8px')]}
])
)
# Display the styled DataFrame
styled_null_summary
| Null Values | Percentage | |
|---|---|---|
| recipe | 0 | 0.00% |
| calories | 52 | 5.49% |
| carbohydrate | 52 | 5.49% |
| sugar | 52 | 5.49% |
| protein | 52 | 5.49% |
| category | 0 | 0.00% |
| servings | 0 | 0.00% |
| high_traffic | 373 | 39.39% |
Insights:¶
High Traffic Column:¶
This column has a significant amount of missing data (39.39%). This might indicate that the data is either not collected or is not applicable for a large portion of the dataset. Handling this column carefully is crucial to avoid skewing analysis results.
Nutritional Values:¶
Columns related to nutritional values (Calories, Carbohydrate, Sugar, Protein) each have 5.49% missing values. While not excessively high, this amount of missing data should be imputed to ensure robust analysis.
Complete Data Columns:¶
The Recipe, Category, and Servings columns have no missing values, ensuring that these attributes are fully accounted for in the dataset.
2.4.2 | Imputating Missing Values
# Define columns
numerical_columns = ['calories', 'carbohydrate', 'sugar', 'protein']
target_column = 'high_traffic'
# Step 1: Impute missing values in numerical columns with median
imputer = SimpleImputer(strategy='median')
# Impute missing values
df[numerical_columns] = imputer.fit_transform(df[numerical_columns])
# Step 2: Apply log transformation to skewed columns
df['calories_log'] = np.log1p(df['calories'])
df['carbohydrate_log'] = np.log1p(df['carbohydrate'])
df['sugar_log'] = np.log1p(df['sugar'])
df['protein_log'] = np.log1p(df['protein'])
# Impute missing values in transformed columns
df['calories_log'] = imputer.fit_transform(df[['calories_log']])
df['carbohydrate_log'] = imputer.fit_transform(df[['carbohydrate_log']])
df['sugar_log'] = imputer.fit_transform(df[['sugar_log']])
df['protein_log'] = imputer.fit_transform(df[['protein_log']])
# Reverse transformation to original scale
df['calories'] = np.expm1(df['calories_log'])
df['carbohydrate'] = np.expm1(df['carbohydrate_log'])
df['sugar'] = np.expm1(df['sugar_log'])
df['protein'] = np.expm1(df['protein_log'])
# Drop the log-transformed columns
df.drop(columns=['calories_log', 'carbohydrate_log', 'sugar_log', 'protein_log'], inplace=True)
# For target variable 'high_traffic'
# Option 1: Remove rows with missing 'high_traffic' values
df.dropna(subset=[target_column], inplace=True)
print("Null Values Have Been Handled")
Null Values Have Been Handled
# Calculate the number of null values and their percentages
null_counts = df.isnull().sum()
total_rows = len(df)
null_percentages = (null_counts / total_rows) * 100
# Create a DataFrame to display the counts and percentages
null_summary = pd.DataFrame({
'Null Values': null_counts,
'Percentage': null_percentages
})
# Apply stylish formatting with custom colors
styled_null_summary = (
null_summary.style
.format({'Percentage': '{:.2f}%'}) # Format percentage to two decimal places
.background_gradient(cmap='coolwarm', subset=['Percentage']) # Apply a gradient to the 'Percentage' column
.highlight_max(subset=['Null Values'], color='lightcoral') # Highlight the row with the maximum null values
.set_caption('Summary of Null Values and Their Percentages') # Add a title to the table
.set_table_styles([
{'selector': 'thead th', 'props': [('background-color', '#FFE4E1'), # Header background color
('color', 'black'), # Header text color
('font-weight', 'bold')]},
{'selector': 'tbody tr:hover', 'props': [('background-color', '#FFE4E1')]}, # Hover effect with background color
{'selector': 'tbody td', 'props': [('background-color', '#FFE4E1'), # Table body background color
('color', 'black'), # Table body text color
('border', '1px solid black'), # Border color
('padding', '8px')]}
])
)
# Display the styled DataFrame
styled_null_summary
| Null Values | Percentage | |
|---|---|---|
| recipe | 0 | 0.00% |
| calories | 0 | 0.00% |
| carbohydrate | 0 | 0.00% |
| sugar | 0 | 0.00% |
| protein | 0 | 0.00% |
| category | 0 | 0.00% |
| servings | 0 | 0.00% |
| high_traffic | 0 | 0.00% |
2.5 | Duplicated Values Handling¶
# Calculate the number of duplicated rows
duplicate_count = df.duplicated().sum()
total_rows = len(df)
duplicate_percentage = (duplicate_count / total_rows) * 100
# Create a DataFrame to display the counts and percentages of duplicated rows
duplicate_summary = pd.DataFrame({
'Duplicated Rows': [duplicate_count],
'Percentage': [duplicate_percentage]
})
# Apply stylish formatting with custom colors
styled_duplicate_summary = (
duplicate_summary.style
.format({'Percentage': '{:.2f}%'}) # Format percentage to two decimal places
.background_gradient(cmap='coolwarm', subset=['Percentage']) # Apply a gradient to the 'Percentage' column
.highlight_max(subset=['Duplicated Rows'], color='lightcoral') # Highlight the row with the maximum duplicated rows
.set_caption('Summary of Duplicated Values and Their Percentages') # Add a title to the table
.set_table_styles([
{'selector': 'thead th', 'props': [('background-color', '#FFE4E1'), # Header background color
('color', 'black'), # Header text color
('font-weight', 'bold')]},
{'selector': 'tbody tr:hover', 'props': [('background-color', '#FFE4E1')]}, # Hover effect with background color
{'selector': 'tbody td', 'props': [('background-color', '#FFE4E1'), # Table body background color
('color', 'black'), # Table body text color
('border', '1px solid black'), # Border color
('padding', '8px')]}
])
)
# Display the styled DataFrame
styled_duplicate_summary
| Duplicated Rows | Percentage | |
|---|---|---|
| 0 | 0 | 0.00% |
2.6 | Visualizations¶
2.6.1 | Numeric Features Visualization¶
# Customize the color palette
colors = ['#eac086', '#ffcd94', '#e57373']
# Create subplots
fig, ax = plt.subplots(6, 3, figsize=(50, 70))
for index, column in enumerate(df.select_dtypes(include='number').columns):
# Distribution Plot with KDE
sns.histplot(df[column], kde=True, color=colors[0], alpha=0.9, ax=ax[index, 0], bins=30, edgecolor='black')
ax[index, 0].set_title(f'Distribution Plot of {column}', fontsize=14, weight='bold')
ax[index, 0].set_xlabel(column, fontsize=12)
ax[index, 0].set_ylabel('Frequency', fontsize=12)
ax[index, 0].grid(True)
# Boxplot
sns.boxplot(x=df[column], ax=ax[index, 1], color=colors[1], saturation=0.9)
ax[index, 1].set_title(f'Box Plot of {column}', fontsize=14, weight='bold')
ax[index, 1].set_xlabel(column, fontsize=12)
ax[index, 1].grid(True)
# Q-Q Plot for Normality Check
stats.probplot(df[column].dropna(), plot=ax[index, 2])
ax[index, 2].get_lines()[1].set_color(colors[2]) # Line of the Q-Q plot
ax[index, 2].get_lines()[1].set_linewidth(2)
ax[index, 2].set_title(f'Q-Q Plot of {column}', fontsize=14, weight='bold')
ax[index, 2].grid(True)
# Improve overall layout and add a main title
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.subplots_adjust(top=0.95, hspace=0.4)
plt.suptitle("Visualizing Continuous Columns", fontsize=50, weight='bold', color='black')
plt.show()
2.6.2 | Categorical Features Visualization¶
# Create subplots for categorical columns
fig, ax = plt.subplots(len(df.select_dtypes(include='object').columns), 1, figsize=(10, 20))
for index, column in enumerate(df.select_dtypes(include='object').columns):
# Count Plot for categorical columns
sns.countplot(x=column, data=df, ax=ax[index], palette=[colors[0], colors[1]])
ax[index].set_title(f'Count Plot of {column}', fontsize=14, weight='bold')
ax[index].set_xlabel(column, fontsize=12)
ax[index].set_ylabel('Count', fontsize=12)
ax[index].grid(True)
# Improve overall layout and add a main title
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.subplots_adjust(top=0.95, hspace=0.4)
plt.suptitle("Visualizing Categorical Columns", fontsize=20, weight='bold', color='black')
plt.show()
2.6.3 | Relational Visualizations
- Correlation Matrix
- Scatter Plot
# Define color palette
colors = ['#eac086', '#FFE4E1', '#F0F8FF']
# Set the background color for all plots
plt.style.use('dark_background')
# Dropping non-numeric columns for correlation
numeric_df = df[['calories', 'carbohydrate', 'sugar', 'protein', 'servings']]
# 1. Correlation Heatmap
plt.figure(figsize=(12, 8))
heatmap = sns.heatmap(numeric_df.corr(), annot=True, cmap=colors, fmt='.2f',
linewidths=0.5, linecolor='black', cbar_kws={'shrink': 0.8})
plt.title('Correlation Heatmap of Nutritional Variables', fontsize=16, weight='bold', color='white')
plt.xticks(ticks=range(len(numeric_df.columns)), labels=numeric_df.columns, rotation=45, fontsize=12, color='white')
plt.yticks(ticks=range(len(numeric_df.columns)), labels=numeric_df.columns, fontsize=12, color='white')
plt.show()
# 2. Scatter Plot: Calories vs. Protein by Category
plt.figure(figsize=(12, 8))
scatter_plot = sns.scatterplot(data=df, x='calories', y='protein', hue='category', palette=colors, s=100, edgecolor='black')
plt.title('Scatter Plot of Calories vs. Protein by Recipe Category', fontsize=16, weight='bold', color='white')
plt.xlabel('Calories', fontsize=14, weight='bold', color='white')
plt.ylabel('Protein', fontsize=14, weight='bold', color='white')
plt.xticks(fontsize=12, color='white')
plt.yticks(fontsize=12, color='white')
plt.legend(title='Category', title_fontsize='13', fontsize='11', loc='upper right', frameon=True, edgecolor='black')
plt.grid(True, linestyle='--', alpha=0.7)
plt.show()
2.6.4 | More Relational Visualizations
- Pair Plots
- Box Plots
- Count Plots
# Define color palette
colors = ['#eac086', '#FFE4E1', '#F0F8FF']
# 1. Pair Plot
plt.figure(figsize=(12, 8))
pair_plot = sns.pairplot(df[['calories', 'carbohydrate', 'sugar', 'protein', 'servings']],
kind='scatter',
diag_kind='hist',
palette=colors)
plt.suptitle('Pair Plot of Nutritional Variables', y=1.02, fontsize=16, weight='bold', color='white')
plt.subplots_adjust(top=0.95)
plt.show()
# 2. Box Plot: Distribution of Calories by Category
plt.figure(figsize=(12, 6))
box_plot = sns.boxplot(data=df, x='category', y='calories', palette=colors)
plt.title('Box Plot of Calories Distribution by Recipe Category', fontsize=16, weight='bold', color='white')
plt.xlabel('Recipe Category', fontsize=14, weight='bold', color='white')
plt.ylabel('Calories', fontsize=14, weight='bold', color='white')
plt.xticks(rotation=45, fontsize=12, color='white')
plt.yticks(fontsize=12, color='white')
plt.grid(True, linestyle='--', alpha=0.7, color='white')
plt.show()
# 3. Count Plot: Distribution of Recipe Categories
plt.figure(figsize=(10, 5))
count_plot = sns.countplot(data=df, x='category', palette=colors)
plt.title('Count Plot of Recipe Categories Distribution', fontsize=16, weight='bold', color='white')
plt.xlabel('Recipe Category', fontsize=14, weight='bold', color='white')
plt.ylabel('Count', fontsize=14, weight='bold', color='white')
plt.xticks(rotation=45, fontsize=12, color='white')
plt.yticks(fontsize=12, color='white')
plt.grid(True, linestyle='--', alpha=0.7, color='white')
plt.show()
<Figure size 1200x800 with 0 Axes>
2.7 | Skewness Handling
# Step 1: Calculate Skewness Before Transformation
# Select only numeric columns for skewness calculation
numeric_columns = df.select_dtypes(include='number').columns
skewness_before = df[numeric_columns].skew(axis=0).sort_values()
skewness_df_before = pd.DataFrame(skewness_before, columns=['Skewness Before'])
# Step 2: Apply Transformations and Store the Transformed Features
transformed_features = {}
skewness_after = []
for col in numeric_columns:
# Check if column values are all positive (required for Box-Cox transformation)
if (df[col] > 0).all():
transformed_data, fitted_lambda = boxcox(df[col].dropna())
transformed_features[col] = transformed_data # Store transformed data
skewness_after.append({'Column': col, 'Skewness After': skew(transformed_data)})
else:
# Apply log transformation for non-positive values (handle zeros by adding a small constant)
transformed_data = np.log1p(df[col] - df[col].min() + 1)
transformed_features[col] = transformed_data
skewness_after.append({'Column': col, 'Skewness After': skew(transformed_data)})
# Convert skewness after transformation to DataFrame
skewness_df_after = pd.DataFrame(skewness_after).set_index('Column')
# Step 3: Use Pandas Styling to Display Skewness Tables Before and After Transformation
styled_skewness_before = (
skewness_df_before.style
.background_gradient(cmap='coolwarm')
.set_properties(**{
'background-color': '#FFE4E1', # Set custom background color
'color': 'black', # Set text color to black
'border': '1px solid black', # Border color
'padding': '8px' # Padding for better readability
})
.set_caption('------- Column Skewness Before Transformation ------')
)
styled_skewness_after = (
skewness_df_after.style
.background_gradient(cmap='coolwarm')
.set_properties(**{
'background-color': '#F0F8FF', # Set custom background color
'color': 'black', # Set text color to black
'border': '1px solid black', # Border color
'padding': '8px' # Padding for better readability
})
.set_caption('---- Column Skewness After Transformation ---')
)
# Display the styled DataFrames
display(styled_skewness_before)
display(styled_skewness_after)
| Skewness Before | |
|---|---|
| servings | -0.051301 |
| recipe | -0.004502 |
| calories | 1.875225 |
| protein | 3.778232 |
| carbohydrate | 4.054543 |
| sugar | 4.409599 |
| Skewness After | |
|---|---|
| Column | |
| recipe | -0.280374 |
| calories | -0.025410 |
| carbohydrate | 0.025765 |
| sugar | 0.024352 |
| protein | 0.299524 |
| servings | -0.209956 |
2.8 | Outliers Handling
# Define numerical columns for analysis
numerical_columns = ['calories', 'carbohydrate', 'sugar', 'protein']
# 🔍 **Feature Scaling** - Standardization using MinMaxScaler
scaler = MinMaxScaler()
df_scaled = df.copy()
df_scaled[numerical_columns] = scaler.fit_transform(df[numerical_columns])
# 🌲 **Initialize the Isolation Forest Model** - Detect anomalies with advanced settings
iso_forest = IsolationForest(
n_estimators=100, # Number of base estimators
max_samples='auto', # Number of samples to draw from X to train each base estimator
contamination='auto', # Proportion of outliers in the data set
max_features=1.0, # Number of features to draw from X to train each base estimator
bootstrap=False, # Whether samples are drawn with replacement
n_jobs=-1, # Number of parallel jobs
random_state=42 # Seed for random number generator
)
# Fit the model
df_scaled['outlier'] = iso_forest.fit_predict(df_scaled[numerical_columns])
# -1 indicates an outlier, 1 indicates a normal point
outliers = df_scaled[df_scaled['outlier'] == -1]
print(f"🌟 Number of outliers detected: {len(outliers)}")
# **Stylize DataFrame** - Highlight outliers with background color #eac086
def highlight_outliers(row):
color = '#eac086' if row['outlier'] == -1 else ''
return ['background-color: {}'.format(color) for _ in row]
# Apply styling to DataFrame
styled_df = df_scaled.style.apply(highlight_outliers, axis=1)
# Display the styled DataFrame
styled_df
🌟 Number of outliers detected: 78
| recipe | calories | carbohydrate | sugar | protein | category | servings | high_traffic | outlier | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 6 | High | 1 |
| 1 | 2 | 0.012162 | 0.072645 | 0.004947 | 0.002532 | Potato | 4 | High | 1 |
| 3 | 4 | 0.033343 | 0.057561 | 0.293956 | 0.000055 | Beverages | 4 | High | 1 |
| 5 | 6 | 0.237798 | 0.006467 | 0.012483 | 0.148420 | One Dish Meal | 2 | High | 1 |
| 8 | 9 | 0.185273 | 0.007070 | 0.025575 | 0.010430 | Pork | 6 | High | 1 |
| 12 | 13 | 0.094461 | 0.044232 | 0.011798 | 0.007073 | Potato | 4 | High | 1 |
| 13 | 14 | 0.008634 | 0.021644 | 0.078475 | 0.026338 | Vegetable | 4 | High | 1 |
| 14 | 15 | 0.074676 | 0.012557 | 0.076039 | 0.041749 | Meat | 4 | High | 1 |
| 15 | 16 | 0.108852 | 0.004940 | 0.035546 | 0.219369 | Meat | 6 | High | 1 |
| 16 | 17 | 0.156280 | 0.003469 | 0.022378 | 0.168070 | Meat | 2 | High | 1 |
| 17 | 18 | 0.583536 | 0.000132 | 0.002892 | 0.091287 | Meat | 1 | High | -1 |
| 18 | 19 | 0.375313 | 0.008711 | 0.005176 | 0.009605 | Meat | 6 | High | 1 |
| 20 | 21 | 0.003138 | 0.032825 | 0.062034 | 0.029750 | Potato | 6 | High | 1 |
| 24 | 25 | 0.399657 | 0.002828 | 0.067514 | 0.034594 | Breakfast | 1 | High | 1 |
| 25 | 26 | 0.019323 | 0.042082 | 0.086543 | 0.095745 | One Dish Meal | 4 | High | 1 |
| 26 | 27 | 0.141445 | 0.097419 | 0.211372 | 0.193472 | Pork | 2 | High | -1 |
| 27 | 28 | 0.197741 | 0.024680 | 0.013929 | 0.038116 | Potato | 4 | High | 1 |
| 28 | 29 | 0.204844 | 0.118102 | 0.020018 | 0.013650 | Potato | 2 | High | 1 |
| 29 | 30 | 0.056651 | 0.063255 | 0.135942 | 0.605845 | One Dish Meal | 2 | High | -1 |
| 30 | 31 | 0.074277 | 0.099229 | 0.047496 | 0.088948 | Pork | 2 | High | 1 |
| 33 | 34 | 0.171043 | 0.002715 | 0.011417 | 0.008174 | Lunch/Snacks | 6 | High | 1 |
| 34 | 35 | 0.198044 | 0.038990 | 0.001446 | 0.017173 | Breakfast | 6 | High | 1 |
| 39 | 40 | 0.015372 | 0.008654 | 0.002968 | 0.016237 | Vegetable | 4 | High | 1 |
| 40 | 41 | 0.213843 | 0.026641 | 0.081367 | 0.109231 | Chicken Breast | 6 | High | 1 |
| 45 | 46 | 0.028649 | 0.024567 | 0.012255 | 0.009467 | Vegetable | 6 | High | 1 |
| 49 | 50 | 0.593320 | 0.085767 | 0.000457 | 0.135871 | Breakfast | 1 | High | -1 |
| 51 | 52 | 0.440278 | 0.036822 | 0.008144 | 0.050088 | Pork | 4 | High | 1 |
| 53 | 54 | 0.025610 | 0.089444 | 0.124448 | 0.124257 | Pork | 4 | High | 1 |
| 54 | 55 | 0.045208 | 0.062765 | 0.012863 | 0.239735 | Pork | 6 | High | 1 |
| 55 | 56 | 0.730280 | 0.048945 | 0.003882 | 0.223938 | Pork | 1 | High | -1 |
| 56 | 57 | 0.016184 | 0.114821 | 0.105115 | 0.029723 | Vegetable | 4 | High | 1 |
| 58 | 59 | 0.002096 | 0.106337 | 0.042548 | 0.005834 | Lunch/Snacks | 6 | High | 1 |
| 60 | 61 | 0.212263 | 0.130885 | 0.059065 | 0.008587 | Dessert | 4 | High | 1 |
| 62 | 63 | 0.055030 | 0.079979 | 0.012102 | 0.122138 | One Dish Meal | 2 | High | 1 |
| 63 | 64 | 0.279758 | 0.166896 | 0.003501 | 0.376734 | Chicken | 1 | High | -1 |
| 64 | 65 | 0.194799 | 0.003186 | 0.019257 | 0.011669 | Meat | 4 | High | 1 |
| 74 | 75 | 0.110270 | 0.157922 | 0.123535 | 0.005697 | Dessert | 1 | High | 1 |
| 75 | 76 | 0.263955 | 0.046626 | 0.004262 | 0.085507 | Potato | 4 | High | 1 |
| 76 | 77 | 0.191860 | 0.069289 | 0.022758 | 0.053803 | One Dish Meal | 1 | High | 1 |
| 79 | 80 | 0.006803 | 0.066819 | 0.016441 | 0.032530 | Breakfast | 1 | High | 1 |
| 80 | 81 | 0.028019 | 0.062313 | 0.017735 | 0.030823 | Breakfast | 4 | High | 1 |
| 81 | 82 | 0.022558 | 0.015008 | 0.070178 | 0.039960 | Breakfast | 4 | High | 1 |
| 82 | 83 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Meat | 4 | High | 1 |
| 84 | 85 | 0.029261 | 0.056430 | 0.004262 | 0.014421 | Chicken | 2 | High | 1 |
| 85 | 86 | 0.228035 | 0.089708 | 0.030979 | 0.006577 | Meat | 1 | High | 1 |
| 86 | 87 | 0.038849 | 0.044156 | 0.003501 | 0.170850 | Meat | 4 | High | 1 |
| 88 | 89 | 0.066428 | 0.008409 | 0.022835 | 0.129073 | Breakfast | 4 | High | 1 |
| 89 | 90 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 6 | High | 1 |
| 91 | 92 | 0.036729 | 0.001603 | 0.055336 | 0.015604 | Vegetable | 4 | High | 1 |
| 94 | 95 | 0.284490 | 0.273931 | 0.049246 | 0.008366 | Potato | 4 | High | 1 |
| 96 | 97 | 0.030432 | 0.026924 | 0.360100 | 0.015604 | Dessert | 1 | High | 1 |
| 97 | 98 | 0.716326 | 0.015196 | 0.036307 | 0.078380 | One Dish Meal | 2 | High | -1 |
| 98 | 99 | 0.045308 | 0.054960 | 0.044451 | 0.023173 | Breakfast | 2 | High | 1 |
| 101 | 102 | 0.068427 | 0.165256 | 0.002892 | 0.107662 | Vegetable | 4 | High | 1 |
| 103 | 104 | 0.370206 | 0.006863 | 0.114858 | 0.125550 | Meat | 4 | High | 1 |
| 105 | 106 | 0.136355 | 0.031713 | 0.018800 | 0.000743 | Beverages | 1 | High | 1 |
| 107 | 108 | 0.067636 | 0.004242 | 0.069341 | 0.005972 | Vegetable | 1 | High | 1 |
| 108 | 109 | 0.210780 | 0.072626 | 0.076496 | 0.015549 | Dessert | 6 | High | 1 |
| 109 | 110 | 0.055467 | 0.060672 | 0.054346 | 0.016760 | Lunch/Snacks | 4 | High | 1 |
| 111 | 112 | 0.140929 | 0.058994 | 0.105191 | 0.082177 | Meat | 1 | High | 1 |
| 112 | 113 | 0.247389 | 0.093026 | 0.009210 | 0.015742 | Potato | 4 | High | 1 |
| 113 | 114 | 0.026371 | 0.092027 | 0.174608 | 0.013430 | Meat | 2 | High | 1 |
| 114 | 115 | 0.061152 | 0.020061 | 0.016745 | 0.008311 | Lunch/Snacks | 2 | High | 1 |
| 115 | 116 | 0.009161 | 0.009691 | 0.018115 | 0.012247 | Vegetable | 4 | High | 1 |
| 116 | 117 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Chicken Breast | 6 | High | 1 |
| 119 | 120 | 0.152471 | 0.030487 | 0.078703 | 0.000028 | Vegetable | 4 | High | 1 |
| 120 | 121 | 0.383637 | 0.059239 | 0.099026 | 0.003660 | Meat | 2 | High | 1 |
| 121 | 122 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Dessert | 2 | High | 1 |
| 124 | 125 | 1.000000 | 0.006580 | 0.014310 | 0.493230 | Pork | 6 | High | -1 |
| 125 | 126 | 0.062040 | 0.018119 | 0.052215 | 0.042905 | Breakfast | 4 | High | 1 |
| 126 | 127 | 0.111450 | 0.149230 | 0.012863 | 0.019347 | One Dish Meal | 4 | High | 1 |
| 127 | 128 | 0.330576 | 0.359754 | 0.031359 | 0.266705 | One Dish Meal | 4 | High | -1 |
| 131 | 132 | 0.055040 | 0.026735 | 0.077714 | 0.010485 | Potato | 4 | High | 1 |
| 132 | 133 | 0.032169 | 0.006995 | 0.223778 | 0.012715 | Dessert | 4 | High | 1 |
| 134 | 135 | 0.035827 | 0.084051 | 0.038895 | 0.007954 | Vegetable | 4 | High | 1 |
| 135 | 136 | 0.039854 | 0.294500 | 0.060055 | 0.136586 | One Dish Meal | 4 | High | -1 |
| 136 | 137 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | One Dish Meal | 2 | High | 1 |
| 137 | 138 | 0.008228 | 0.112370 | 0.002283 | 0.390880 | Chicken Breast | 4 | High | -1 |
| 138 | 139 | 0.033009 | 0.097871 | 0.020094 | 0.035970 | Breakfast | 6 | High | 1 |
| 139 | 140 | 0.004639 | 0.173438 | 0.006698 | 0.123624 | One Dish Meal | 1 | High | 1 |
| 142 | 143 | 0.032441 | 0.048926 | 0.181002 | 0.004101 | Dessert | 4 | High | 1 |
| 144 | 145 | 0.102131 | 0.002621 | 0.084792 | 0.069325 | Breakfast | 2 | High | 1 |
| 146 | 147 | 0.078957 | 0.075228 | 0.008220 | 0.018329 | Potato | 4 | High | 1 |
| 148 | 149 | 0.070836 | 0.116556 | 0.021921 | 0.039768 | Chicken | 4 | High | 1 |
| 149 | 150 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Potato | 2 | High | 1 |
| 150 | 151 | 0.098559 | 0.039725 | 0.011569 | 0.011284 | Breakfast | 6 | High | 1 |
| 151 | 152 | 0.107111 | 0.081393 | 0.008525 | 0.009247 | Vegetable | 1 | High | 1 |
| 152 | 153 | 0.061544 | 0.008126 | 0.131527 | 0.050886 | Vegetable | 4 | High | 1 |
| 154 | 155 | 0.064793 | 0.064594 | 0.042168 | 0.008669 | Breakfast | 4 | High | 1 |
| 158 | 159 | 0.115384 | 0.044967 | 0.021008 | 0.105075 | Vegetable | 6 | High | 1 |
| 159 | 160 | 0.267469 | 0.191746 | 0.019105 | 0.014091 | Dessert | 4 | High | 1 |
| 160 | 161 | 0.121630 | 0.024114 | 0.007688 | 0.017586 | One Dish Meal | 1 | High | 1 |
| 161 | 162 | 0.295598 | 0.137880 | 0.111052 | 0.009082 | Dessert | 6 | High | 1 |
| 163 | 164 | 0.130677 | 0.026226 | 0.004262 | 0.120211 | Chicken | 4 | High | 1 |
| 165 | 166 | 0.062274 | 0.013763 | 0.018192 | 0.286410 | Chicken Breast | 1 | High | 1 |
| 167 | 168 | 0.001469 | 0.450668 | 0.148653 | 0.010348 | Dessert | 1 | High | -1 |
| 169 | 170 | 0.208884 | 0.043308 | 0.300731 | 0.071967 | Breakfast | 4 | High | -1 |
| 170 | 171 | 0.148369 | 0.027640 | 0.031664 | 0.090241 | One Dish Meal | 2 | High | 1 |
| 173 | 174 | 0.062074 | 0.023322 | 0.187548 | 0.013981 | Dessert | 2 | High | 1 |
| 174 | 175 | 0.005658 | 0.010615 | 0.109606 | 0.054877 | Breakfast | 6 | High | 1 |
| 179 | 180 | 0.141163 | 0.052904 | 0.026564 | 0.354579 | Chicken Breast | 4 | High | 1 |
| 180 | 181 | 0.015359 | 0.022399 | 0.060283 | 0.002945 | Potato | 6 | High | 1 |
| 181 | 182 | 0.199871 | 0.043609 | 0.159689 | 0.015494 | Dessert | 6 | High | 1 |
| 182 | 183 | 0.240651 | 0.106752 | 0.024281 | 0.009577 | Vegetable | 4 | High | 1 |
| 185 | 186 | 0.435880 | 0.290635 | 0.004415 | 0.001266 | Lunch/Snacks | 4 | High | -1 |
| 186 | 187 | 0.177348 | 0.011030 | 0.101081 | 0.038419 | Lunch/Snacks | 1 | High | 1 |
| 187 | 188 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 4 | High | 1 |
| 188 | 189 | 0.062535 | 0.062237 | 0.339930 | 0.015081 | Dessert | 4 | High | 1 |
| 189 | 190 | 0.182231 | 0.044722 | 0.017278 | 0.043511 | Breakfast | 1 | High | 1 |
| 193 | 194 | 0.171229 | 0.006976 | 0.143401 | 0.022264 | Chicken Breast | 2 | High | 1 |
| 195 | 196 | 0.115673 | 0.070420 | 0.049627 | 0.063628 | Lunch/Snacks | 1 | High | 1 |
| 197 | 198 | 0.095637 | 0.018515 | 0.098341 | 0.073618 | Chicken Breast | 6 | High | 1 |
| 199 | 200 | 0.017957 | 0.005298 | 0.022454 | 0.242762 | Meat | 4 | High | 1 |
| 200 | 201 | 0.030827 | 0.020362 | 0.017126 | 0.012439 | Vegetable | 2 | High | 1 |
| 201 | 202 | 0.039172 | 0.023586 | 0.004795 | 0.021824 | Vegetable | 6 | High | 1 |
| 203 | 204 | 0.022720 | 0.019646 | 0.008068 | 0.024246 | One Dish Meal | 2 | High | 1 |
| 204 | 205 | 0.088851 | 0.009785 | 0.062567 | 0.079948 | Breakfast | 1 | High | 1 |
| 205 | 206 | 0.097589 | 0.064141 | 0.012331 | 0.002532 | Lunch/Snacks | 6 | High | 1 |
| 206 | 207 | 0.189809 | 0.002036 | 0.044832 | 0.512384 | Pork | 4 | High | -1 |
| 208 | 209 | 0.003465 | 0.084089 | 0.134952 | 0.037181 | Pork | 4 | High | 1 |
| 209 | 210 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Dessert | 2 | High | 1 |
| 210 | 211 | 0.400913 | 0.000207 | 0.033110 | 0.026530 | Potato | 1 | High | 1 |
| 211 | 212 | 0.106667 | 0.091536 | 0.266403 | 0.110414 | Pork | 4 | High | 1 |
| 212 | 213 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Dessert | 4 | High | 1 |
| 213 | 214 | 0.280849 | 0.010162 | 0.062643 | 0.103121 | Meat | 1 | High | 1 |
| 214 | 215 | 0.102957 | 0.018006 | 0.018953 | 0.345387 | Chicken | 6 | High | 1 |
| 216 | 217 | 0.008731 | 0.076566 | 0.012635 | 0.008972 | Lunch/Snacks | 2 | High | 1 |
| 217 | 218 | 0.444631 | 0.168122 | 0.108160 | 0.220250 | Chicken Breast | 4 | High | -1 |
| 218 | 219 | 0.057229 | 0.000943 | 0.225833 | 0.013375 | Dessert | 6 | High | 1 |
| 220 | 221 | 0.215295 | 0.044024 | 0.091186 | 0.070976 | Breakfast | 2 | High | 1 |
| 222 | 223 | 0.016508 | 0.009616 | 0.002131 | 0.001404 | Lunch/Snacks | 4 | High | 1 |
| 224 | 225 | 0.065987 | 0.036313 | 0.041711 | 0.010210 | Potato | 4 | High | 1 |
| 225 | 226 | 0.045997 | 0.010275 | 0.003273 | 0.158961 | Pork | 6 | High | 1 |
| 226 | 227 | 0.930179 | 0.012010 | 0.016441 | 0.077609 | Pork | 6 | High | -1 |
| 227 | 228 | 0.042896 | 0.299572 | 0.013320 | 0.063491 | Breakfast | 1 | High | 1 |
| 228 | 229 | 0.000000 | 0.034107 | 0.084792 | 0.240368 | Lunch/Snacks | 2 | High | 1 |
| 231 | 232 | 0.085224 | 0.061822 | 0.034708 | 0.027686 | Vegetable | 2 | High | 1 |
| 233 | 234 | 0.180170 | 0.003677 | 0.005404 | 0.114349 | Chicken Breast | 6 | High | 1 |
| 236 | 237 | 0.166164 | 0.101246 | 0.049627 | 0.009522 | Dessert | 6 | High | 1 |
| 238 | 239 | 0.200133 | 0.021588 | 0.060359 | 0.037539 | Chicken | 4 | High | 1 |
| 241 | 242 | 0.272500 | 0.105752 | 0.018724 | 0.007623 | Potato | 4 | High | 1 |
| 242 | 243 | 0.533424 | 0.137314 | 0.011265 | 0.110992 | Chicken Breast | 4 | High | -1 |
| 243 | 244 | 0.224979 | 0.210732 | 0.038286 | 0.000991 | Potato | 6 | High | 1 |
| 244 | 245 | 0.055161 | 0.021380 | 0.004491 | 0.000661 | Potato | 6 | High | 1 |
| 245 | 246 | 0.116089 | 0.052697 | 0.017126 | 0.052427 | Potato | 2 | High | 1 |
| 246 | 247 | 0.024719 | 0.031882 | 0.007155 | 0.009275 | Vegetable | 2 | High | 1 |
| 251 | 252 | 0.044355 | 0.097174 | 0.058228 | 0.016650 | Lunch/Snacks | 4 | High | 1 |
| 252 | 253 | 0.015100 | 0.013047 | 0.009286 | 0.006522 | Vegetable | 4 | High | 1 |
| 254 | 255 | 0.123495 | 0.003054 | 0.008373 | 0.056555 | Chicken | 4 | High | 1 |
| 256 | 257 | 0.234900 | 0.059070 | 0.043309 | 0.009852 | Potato | 2 | High | 1 |
| 257 | 258 | 0.197851 | 0.131809 | 0.008905 | 0.004954 | Vegetable | 1 | High | 1 |
| 259 | 260 | 0.232185 | 0.004921 | 0.027630 | 0.003468 | Vegetable | 2 | High | 1 |
| 261 | 262 | 0.047315 | 0.360452 | 0.015527 | 0.014669 | Potato | 2 | High | -1 |
| 263 | 264 | 0.159081 | 0.006524 | 0.050997 | 0.002559 | Vegetable | 6 | High | 1 |
| 264 | 265 | 0.124834 | 0.065235 | 0.009058 | 0.020586 | Pork | 2 | High | 1 |
| 265 | 266 | 0.215588 | 0.020155 | 0.033947 | 0.016237 | Potato | 1 | High | 1 |
| 266 | 267 | 0.150003 | 0.014386 | 0.169661 | 0.063381 | Meat | 4 | High | 1 |
| 267 | 268 | 0.608214 | 0.018722 | 0.031588 | 0.193747 | Pork | 4 | High | -1 |
| 268 | 269 | 0.143114 | 0.003413 | 0.136855 | 0.003880 | Pork | 1 | High | 1 |
| 270 | 271 | 0.157340 | 0.023963 | 0.036687 | 0.058977 | Chicken Breast | 4 | High | 1 |
| 271 | 272 | 0.149972 | 0.110522 | 0.300274 | 0.030768 | Dessert | 1 | High | 1 |
| 274 | 275 | 0.295447 | 0.009502 | 0.056782 | 0.005064 | Lunch/Snacks | 1 | High | 1 |
| 275 | 276 | 0.205587 | 0.036992 | 0.047039 | 0.018246 | Lunch/Snacks | 6 | High | 1 |
| 276 | 277 | 0.000213 | 0.003526 | 0.028543 | 0.000138 | Vegetable | 2 | High | 1 |
| 278 | 279 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Lunch/Snacks | 4 | High | 1 |
| 280 | 281 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Meat | 1 | High | 1 |
| 281 | 282 | 0.141042 | 0.025604 | 0.014842 | 0.024136 | Vegetable | 4 | High | 1 |
| 282 | 283 | 0.010844 | 0.010162 | 0.050845 | 0.036465 | Vegetable | 6 | High | 1 |
| 283 | 284 | 0.039437 | 0.061577 | 0.076191 | 0.240698 | Pork | 6 | High | 1 |
| 285 | 286 | 0.493900 | 0.142160 | 0.130005 | 0.000991 | Lunch/Snacks | 1 | High | -1 |
| 289 | 290 | 0.076696 | 0.048945 | 0.057162 | 0.032172 | Meat | 4 | High | 1 |
| 290 | 291 | 0.082509 | 0.096344 | 1.000000 | 0.009302 | Dessert | 2 | High | -1 |
| 292 | 293 | 0.646512 | 0.001452 | 0.057771 | 0.244964 | Chicken Breast | 1 | High | -1 |
| 294 | 295 | 0.083266 | 0.012538 | 0.001827 | 0.082067 | Pork | 4 | High | 1 |
| 295 | 296 | 0.054483 | 0.038820 | 0.047876 | 0.013072 | Vegetable | 2 | High | 1 |
| 296 | 297 | 0.167840 | 0.268086 | 0.039580 | 0.003000 | Potato | 6 | High | 1 |
| 299 | 300 | 0.439741 | 0.007768 | 0.002131 | 0.021026 | Pork | 1 | High | 1 |
| 301 | 302 | 0.101078 | 0.021494 | 0.023976 | 0.002697 | Vegetable | 2 | High | 1 |
| 304 | 305 | 0.061706 | 0.028583 | 0.016898 | 0.006963 | Potato | 1 | High | 1 |
| 305 | 306 | 0.223578 | 0.040744 | 0.012787 | 0.000358 | Meat | 2 | High | 1 |
| 306 | 307 | 0.587297 | 0.046306 | 0.040645 | 0.108790 | Meat | 4 | High | 1 |
| 308 | 309 | 0.025387 | 0.066027 | 0.015832 | 0.040401 | Vegetable | 6 | High | 1 |
| 309 | 310 | 0.199906 | 0.072569 | 0.000685 | 0.228011 | Chicken Breast | 1 | High | 1 |
| 310 | 311 | 0.312640 | 0.080130 | 0.009134 | 0.126954 | Pork | 4 | High | 1 |
| 312 | 313 | 0.362394 | 0.022625 | 0.126579 | 0.013540 | One Dish Meal | 2 | High | 1 |
| 316 | 317 | 0.057329 | 0.029620 | 0.032958 | 0.467250 | Meat | 4 | High | -1 |
| 317 | 318 | 0.031684 | 0.016026 | 0.001751 | 0.038915 | Lunch/Snacks | 4 | High | 1 |
| 318 | 319 | 0.162695 | 0.016309 | 0.014462 | 0.041502 | Meat | 4 | High | 1 |
| 319 | 320 | 0.140712 | 0.134901 | 0.043538 | 0.345828 | Chicken Breast | 2 | High | -1 |
| 321 | 322 | 0.026178 | 0.171987 | 0.046506 | 0.008999 | Dessert | 4 | High | 1 |
| 322 | 323 | 0.088638 | 0.265710 | 0.071929 | 0.024136 | Breakfast | 6 | High | 1 |
| 324 | 325 | 0.265456 | 0.180999 | 0.011265 | 0.020696 | Dessert | 4 | High | 1 |
| 326 | 327 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Potato | 4 | High | 1 |
| 327 | 328 | 0.274703 | 0.001565 | 0.003730 | 0.001101 | Vegetable | 4 | High | 1 |
| 328 | 329 | 0.193157 | 0.022248 | 0.068427 | 0.521054 | Pork | 6 | High | -1 |
| 329 | 330 | 0.190198 | 0.074511 | 0.027401 | 0.133587 | Potato | 4 | High | 1 |
| 331 | 332 | 0.333835 | 0.334075 | 0.013625 | 0.017723 | Lunch/Snacks | 2 | High | -1 |
| 332 | 333 | 0.007113 | 0.063896 | 0.041330 | 0.051024 | Lunch/Snacks | 4 | High | 1 |
| 334 | 335 | 0.183033 | 0.059824 | 0.010884 | 0.049428 | Lunch/Snacks | 4 | High | 1 |
| 337 | 338 | 0.047480 | 0.006919 | 0.039808 | 0.174923 | Chicken | 4 | High | 1 |
| 338 | 339 | 0.165998 | 0.049360 | 0.040189 | 0.052097 | Potato | 4 | High | 1 |
| 340 | 341 | 0.430604 | 0.023096 | 0.001218 | 0.001624 | Lunch/Snacks | 4 | High | 1 |
| 341 | 342 | 0.008087 | 0.193235 | 0.108160 | 0.168483 | One Dish Meal | 4 | High | -1 |
| 343 | 344 | 0.329726 | 0.094251 | 0.007383 | 0.025512 | Breakfast | 6 | High | 1 |
| 345 | 346 | 0.064463 | 0.089726 | 0.123991 | 0.216892 | Pork | 1 | High | 1 |
| 346 | 347 | 0.038044 | 0.110805 | 0.344954 | 0.003385 | Dessert | 6 | High | -1 |
| 347 | 348 | 0.143152 | 0.006410 | 0.007916 | 0.027851 | Chicken | 6 | High | 1 |
| 348 | 349 | 0.412634 | 0.077170 | 0.011722 | 0.081269 | Pork | 6 | High | 1 |
| 349 | 350 | 0.032393 | 0.722167 | 0.045441 | 0.109010 | Potato | 1 | High | -1 |
| 350 | 351 | 0.015651 | 0.028866 | 0.024814 | 0.005945 | Breakfast | 6 | High | 1 |
| 351 | 352 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Potato | 4 | High | 1 |
| 352 | 353 | 0.268770 | 0.007315 | 0.002892 | 0.058922 | Pork | 4 | High | 1 |
| 354 | 355 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 4 | High | 1 |
| 355 | 356 | 0.460299 | 0.007183 | 0.064469 | 0.002614 | Vegetable | 6 | High | 1 |
| 356 | 357 | 0.785465 | 0.008428 | 0.031588 | 0.085838 | One Dish Meal | 4 | High | -1 |
| 357 | 358 | 0.089206 | 0.037746 | 0.026184 | 0.250881 | Meat | 4 | High | 1 |
| 358 | 359 | 0.079515 | 0.028564 | 0.052900 | 0.194710 | Chicken | 1 | High | 1 |
| 359 | 360 | 0.046912 | 0.058561 | 0.018192 | 0.018632 | Potato | 2 | High | 1 |
| 360 | 361 | 0.292518 | 0.004770 | 0.011493 | 0.025622 | Lunch/Snacks | 6 | High | 1 |
| 361 | 362 | 0.346784 | 0.006410 | 0.043918 | 0.163006 | Chicken Breast | 4 | High | 1 |
| 362 | 363 | 0.179513 | 0.143932 | 0.096057 | 0.023723 | Potato | 6 | High | 1 |
| 366 | 367 | 0.011986 | 0.031769 | 0.034480 | 0.038639 | Vegetable | 4 | High | 1 |
| 367 | 368 | 0.042287 | 0.382040 | 0.121708 | 0.022402 | Dessert | 6 | High | -1 |
| 368 | 369 | 0.198997 | 0.017723 | 0.033415 | 0.023007 | Chicken Breast | 1 | High | 1 |
| 369 | 370 | 0.068620 | 0.017101 | 0.062110 | 0.027796 | Meat | 6 | High | 1 |
| 371 | 372 | 0.089409 | 0.035634 | 0.038590 | 0.000908 | Vegetable | 6 | High | 1 |
| 372 | 373 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Vegetable | 2 | High | 1 |
| 373 | 374 | 0.301855 | 0.031279 | 0.281930 | 0.058042 | Pork | 4 | High | -1 |
| 375 | 376 | 0.091391 | 0.021965 | 0.088979 | 0.002174 | Vegetable | 2 | High | 1 |
| 376 | 377 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 6 | High | 1 |
| 377 | 378 | 0.062656 | 0.021927 | 0.006850 | 0.203490 | Chicken Breast | 4 | High | 1 |
| 378 | 379 | 0.081136 | 0.081393 | 0.313822 | 0.011751 | Dessert | 1 | High | 1 |
| 379 | 380 | 0.026068 | 0.042384 | 0.062186 | 0.048547 | Meat | 6 | High | 1 |
| 380 | 381 | 0.149404 | 0.121684 | 0.022073 | 0.019705 | Potato | 2 | High | 1 |
| 382 | 383 | 0.094918 | 0.056392 | 0.100548 | 0.020613 | Chicken | 1 | High | 1 |
| 383 | 384 | 0.243817 | 0.085616 | 0.034556 | 0.610717 | Chicken Breast | 6 | High | -1 |
| 385 | 386 | 0.025820 | 0.012104 | 0.065307 | 0.002009 | Beverages | 2 | High | 1 |
| 388 | 389 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Lunch/Snacks | 4 | High | 1 |
| 389 | 390 | 0.087905 | 0.089406 | 0.011950 | 0.013540 | Vegetable | 4 | High | 1 |
| 391 | 392 | 0.037830 | 0.007617 | 0.069189 | 0.024439 | Potato | 6 | High | 1 |
| 395 | 396 | 0.031251 | 0.028281 | 0.040417 | 0.053088 | Breakfast | 1 | High | 1 |
| 396 | 397 | 0.022100 | 0.021211 | 0.094839 | 0.057436 | Potato | 4 | High | 1 |
| 397 | 398 | 0.705882 | 0.004582 | 0.016060 | 0.191601 | Chicken | 2 | High | -1 |
| 398 | 399 | 0.044104 | 0.027753 | 0.022835 | 0.044969 | Pork | 4 | High | 1 |
| 404 | 405 | 0.048526 | 0.128226 | 0.011189 | 0.019127 | Meat | 4 | High | 1 |
| 405 | 406 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Vegetable | 4 | High | 1 |
| 406 | 407 | 0.058396 | 0.079922 | 0.014995 | 0.085590 | Meat | 4 | High | 1 |
| 411 | 412 | 0.212453 | 0.027621 | 0.075126 | 0.008587 | Dessert | 6 | High | 1 |
| 412 | 413 | 0.081845 | 0.006260 | 0.009134 | 0.014861 | Vegetable | 6 | High | 1 |
| 413 | 414 | 0.051857 | 0.222798 | 0.005633 | 0.017256 | Potato | 4 | High | 1 |
| 415 | 416 | 0.161267 | 0.067064 | 0.015071 | 0.056858 | Meat | 4 | High | 1 |
| 417 | 418 | 0.246233 | 0.016874 | 0.014919 | 0.002559 | Vegetable | 4 | High | 1 |
| 418 | 419 | 0.629808 | 0.007334 | 0.013853 | 0.123129 | Chicken Breast | 1 | High | -1 |
| 419 | 420 | 0.184551 | 0.016667 | 0.015756 | 0.105873 | Chicken Breast | 4 | High | 1 |
| 421 | 422 | 0.114269 | 0.048700 | 0.007840 | 0.055730 | Meat | 4 | High | 1 |
| 422 | 423 | 0.082722 | 0.036709 | 0.042853 | 0.006715 | Pork | 4 | High | 1 |
| 424 | 425 | 0.026202 | 0.225494 | 0.003045 | 0.048960 | Lunch/Snacks | 4 | High | 1 |
| 425 | 426 | 0.434641 | 0.009634 | 0.125818 | 0.276475 | Meat | 2 | High | -1 |
| 427 | 428 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Vegetable | 4 | High | 1 |
| 428 | 429 | 0.566677 | 0.190577 | 0.021008 | 0.040924 | Meat | 1 | High | -1 |
| 432 | 433 | 0.344076 | 0.069044 | 0.043005 | 0.046015 | Chicken Breast | 4 | High | 1 |
| 433 | 434 | 0.016790 | 0.123777 | 0.012255 | 0.026282 | Meat | 4 | High | 1 |
| 435 | 436 | 0.104943 | 0.041385 | 0.232836 | 0.032447 | Lunch/Snacks | 4 | High | 1 |
| 436 | 437 | 0.249870 | 0.080488 | 0.012255 | 0.052813 | One Dish Meal | 6 | High | 1 |
| 438 | 439 | 0.052218 | 0.013914 | 0.046278 | 0.110744 | One Dish Meal | 4 | High | 1 |
| 439 | 440 | 0.039210 | 0.028470 | 0.013701 | 0.106231 | Chicken | 1 | High | 1 |
| 440 | 441 | 0.101684 | 0.042308 | 0.226138 | 0.021356 | Dessert | 6 | High | 1 |
| 442 | 443 | 0.037214 | 0.114840 | 0.059218 | 0.152631 | One Dish Meal | 4 | High | 1 |
| 443 | 444 | 0.246621 | 0.046947 | 0.025194 | 0.044336 | Meat | 6 | High | 1 |
| 445 | 446 | 0.128777 | 0.028583 | 0.070102 | 0.006853 | Dessert | 2 | High | 1 |
| 446 | 447 | 0.043230 | 0.017251 | 0.004034 | 0.234808 | Chicken Breast | 2 | High | 1 |
| 449 | 450 | 0.049603 | 0.074662 | 0.066981 | 0.040153 | Pork | 4 | High | 1 |
| 450 | 451 | 0.062845 | 1.000000 | 0.044071 | 0.013788 | Potato | 4 | High | -1 |
| 451 | 452 | 0.530784 | 0.027715 | 0.043386 | 0.082343 | Lunch/Snacks | 4 | High | 1 |
| 454 | 455 | 0.006225 | 0.042026 | 0.039047 | 0.050556 | Potato | 6 | High | 1 |
| 455 | 456 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 6 | High | 1 |
| 456 | 457 | 0.098614 | 0.056355 | 0.021236 | 0.077251 | Breakfast | 4 | High | 1 |
| 457 | 458 | 0.274135 | 0.084504 | 0.017887 | 0.009467 | Potato | 4 | High | 1 |
| 458 | 459 | 0.089192 | 0.170855 | 0.028848 | 0.236157 | One Dish Meal | 1 | High | 1 |
| 459 | 460 | 0.249729 | 0.078527 | 0.011417 | 0.021411 | Potato | 6 | High | 1 |
| 460 | 461 | 0.405854 | 0.000528 | 0.104202 | 0.003908 | One Dish Meal | 4 | High | 1 |
| 461 | 462 | 0.120153 | 0.011595 | 0.014538 | 0.069298 | Vegetable | 2 | High | 1 |
| 462 | 463 | 0.306813 | 0.005600 | 0.037753 | 0.086306 | Breakfast | 4 | High | 1 |
| 464 | 465 | 0.037318 | 0.040800 | 0.001751 | 0.030934 | Vegetable | 1 | High | 1 |
| 467 | 468 | 0.009997 | 0.002451 | 0.502512 | 0.007045 | Dessert | 6 | High | -1 |
| 471 | 472 | 0.427304 | 0.011275 | 0.078779 | 0.032998 | Pork | 4 | High | 1 |
| 472 | 473 | 0.175937 | 0.011746 | 0.054498 | 0.018219 | Dessert | 4 | High | 1 |
| 473 | 474 | 0.023256 | 0.020739 | 0.040417 | 0.111157 | Pork | 1 | High | 1 |
| 474 | 475 | 0.121781 | 0.028903 | 0.205206 | 0.176547 | Chicken Breast | 2 | High | 1 |
| 475 | 476 | 0.018869 | 0.006957 | 0.055183 | 0.002972 | Vegetable | 4 | High | 1 |
| 476 | 477 | 0.190487 | 0.004940 | 0.012863 | 0.021907 | Vegetable | 4 | High | 1 |
| 478 | 479 | 0.187882 | 0.049209 | 0.024052 | 0.053446 | Chicken | 6 | High | 1 |
| 479 | 480 | 0.180717 | 0.117706 | 0.009591 | 0.029860 | Lunch/Snacks | 1 | High | 1 |
| 481 | 482 | 0.135178 | 0.195007 | 0.037753 | 0.002917 | Vegetable | 6 | High | 1 |
| 482 | 483 | 0.316804 | 0.018628 | 0.294185 | 0.000991 | Dessert | 4 | High | -1 |
| 485 | 486 | 0.047851 | 0.037840 | 0.139976 | 0.010871 | Breakfast | 4 | High | 1 |
| 486 | 487 | 0.002550 | 0.102114 | 0.006850 | 0.039025 | Lunch/Snacks | 1 | High | 1 |
| 487 | 488 | 0.445674 | 0.065499 | 0.005633 | 0.471213 | Meat | 2 | High | -1 |
| 494 | 495 | 0.015899 | 0.034333 | 0.001066 | 0.004348 | Vegetable | 4 | High | 1 |
| 495 | 496 | 0.405318 | 0.090914 | 0.005861 | 0.050363 | Pork | 2 | High | 1 |
| 496 | 497 | 0.089478 | 0.043440 | 0.544299 | 0.008862 | Dessert | 2 | High | -1 |
| 497 | 498 | 0.120608 | 0.138125 | 0.135180 | 0.005449 | Dessert | 4 | High | 1 |
| 499 | 500 | 0.114551 | 0.036596 | 0.005176 | 0.034319 | Meat | 1 | High | 1 |
| 502 | 503 | 0.382123 | 0.052678 | 0.030065 | 0.078847 | One Dish Meal | 4 | High | 1 |
| 503 | 504 | 0.182087 | 0.189804 | 0.044680 | 0.026420 | Vegetable | 4 | High | 1 |
| 506 | 507 | 0.294666 | 0.100379 | 0.088446 | 0.017613 | Vegetable | 4 | High | 1 |
| 508 | 509 | 0.046702 | 0.044986 | 0.094154 | 0.040979 | Potato | 1 | High | 1 |
| 511 | 512 | 0.130980 | 0.045419 | 0.021312 | 0.019320 | Potato | 4 | High | 1 |
| 512 | 513 | 0.002495 | 0.008371 | 0.088370 | 0.029585 | Vegetable | 4 | High | 1 |
| 514 | 515 | 0.011088 | 0.002772 | 0.103973 | 0.033273 | Breakfast | 1 | High | 1 |
| 516 | 517 | 0.090847 | 0.073569 | 0.022530 | 0.063408 | Chicken | 4 | High | 1 |
| 517 | 518 | 0.078992 | 0.020155 | 0.043614 | 0.003688 | Vegetable | 4 | High | 1 |
| 520 | 521 | 0.125047 | 0.044288 | 0.003730 | 0.216012 | Chicken Breast | 1 | High | 1 |
| 521 | 522 | 0.206771 | 0.369106 | 0.039884 | 0.063601 | One Dish Meal | 4 | High | -1 |
| 522 | 523 | 0.155730 | 0.040065 | 0.092251 | 0.398255 | Chicken Breast | 2 | High | -1 |
| 524 | 525 | 0.185476 | 0.025321 | 0.006241 | 0.036851 | Chicken Breast | 4 | High | 1 |
| 527 | 528 | 0.003620 | 0.004318 | 0.000533 | 0.202059 | Pork | 6 | High | 1 |
| 529 | 530 | 0.154326 | 0.343049 | 0.277516 | 0.033108 | One Dish Meal | 6 | High | -1 |
| 530 | 531 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Vegetable | 1 | High | 1 |
| 532 | 533 | 0.183959 | 0.001810 | 0.002360 | 0.008724 | Potato | 1 | High | 1 |
| 534 | 535 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Chicken | 2 | High | 1 |
| 535 | 536 | 0.237158 | 0.009634 | 0.008525 | 0.035007 | Vegetable | 4 | High | 1 |
| 536 | 537 | 0.302653 | 0.000000 | 0.063404 | 0.021549 | Chicken Breast | 4 | High | 1 |
| 538 | 539 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Vegetable | 4 | High | 1 |
| 541 | 542 | 0.176501 | 0.004054 | 0.000913 | 0.135238 | Lunch/Snacks | 2 | High | 1 |
| 542 | 543 | 0.017568 | 0.075284 | 0.061653 | 0.050391 | Potato | 4 | High | 1 |
| 543 | 544 | 0.077856 | 0.025283 | 0.003349 | 0.103754 | Potato | 4 | High | 1 |
| 545 | 546 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Chicken Breast | 6 | High | 1 |
| 547 | 548 | 0.590302 | 0.084146 | 0.050693 | 0.090764 | Pork | 4 | High | 1 |
| 548 | 549 | 0.495738 | 0.057618 | 0.043918 | 0.084297 | Lunch/Snacks | 6 | High | 1 |
| 549 | 550 | 0.162867 | 0.018628 | 0.013929 | 0.018081 | Potato | 4 | High | 1 |
| 550 | 551 | 0.319013 | 0.000038 | 0.013092 | 0.033823 | Potato | 4 | High | 1 |
| 552 | 553 | 0.083001 | 0.060635 | 0.010808 | 0.002752 | Vegetable | 2 | High | 1 |
| 553 | 554 | 0.073252 | 0.105224 | 0.091110 | 0.003275 | Chicken Breast | 4 | High | 1 |
| 557 | 558 | 0.011986 | 0.005091 | 0.230705 | 0.013623 | Breakfast | 2 | High | 1 |
| 559 | 560 | 0.075544 | 0.045872 | 0.040493 | 0.003633 | Breakfast | 2 | High | 1 |
| 563 | 564 | 0.007530 | 0.058768 | 0.005556 | 0.192839 | Chicken Breast | 1 | High | 1 |
| 564 | 565 | 0.120119 | 0.051566 | 0.095448 | 0.269402 | One Dish Meal | 4 | High | 1 |
| 565 | 566 | 0.128061 | 0.153642 | 0.028619 | 0.062583 | One Dish Meal | 4 | High | 1 |
| 568 | 569 | 0.144239 | 0.029846 | 0.004415 | 0.027521 | One Dish Meal | 4 | High | 1 |
| 569 | 570 | 0.182723 | 0.005996 | 0.006318 | 0.154640 | Chicken | 1 | High | 1 |
| 571 | 572 | 0.075895 | 0.009107 | 0.022606 | 0.052015 | Vegetable | 4 | High | 1 |
| 572 | 573 | 0.212570 | 0.030694 | 0.032273 | 0.026393 | Chicken Breast | 1 | High | 1 |
| 577 | 578 | 0.008022 | 0.033843 | 0.053966 | 0.026778 | Potato | 2 | High | 1 |
| 578 | 579 | 0.199661 | 0.041743 | 0.028619 | 0.025374 | Potato | 4 | High | 1 |
| 582 | 583 | 0.155427 | 0.036973 | 0.031588 | 0.014779 | Vegetable | 6 | High | 1 |
| 585 | 586 | 0.280715 | 0.129490 | 0.007383 | 0.184115 | Chicken Breast | 2 | High | 1 |
| 587 | 588 | 0.000489 | 0.112823 | 0.049931 | 0.096323 | Pork | 4 | High | 1 |
| 588 | 589 | 0.084876 | 0.023662 | 0.021312 | 0.064289 | Pork | 2 | High | 1 |
| 589 | 590 | 0.275849 | 0.076830 | 0.036611 | 0.121175 | Meat | 6 | High | 1 |
| 590 | 591 | 0.017874 | 0.142084 | 0.376922 | 0.078077 | Pork | 6 | High | -1 |
| 592 | 593 | 0.029905 | 0.014518 | 0.646598 | 0.026007 | Dessert | 1 | High | -1 |
| 593 | 594 | 0.128082 | 0.021908 | 0.042016 | 0.094149 | Pork | 2 | High | 1 |
| 595 | 596 | 0.105566 | 0.036992 | 0.078627 | 0.078077 | Pork | 2 | High | 1 |
| 599 | 600 | 0.310544 | 0.012199 | 0.054498 | 0.114487 | Pork | 2 | High | 1 |
| 600 | 601 | 0.170341 | 0.061879 | 0.032197 | 0.039107 | Potato | 6 | High | 1 |
| 603 | 604 | 0.035039 | 0.061163 | 0.087000 | 0.008752 | Vegetable | 4 | High | 1 |
| 604 | 605 | 0.003228 | 0.012010 | 0.063556 | 0.136421 | Meat | 1 | High | 1 |
| 605 | 606 | 0.033907 | 0.002640 | 0.058761 | 0.022402 | Chicken | 4 | High | 1 |
| 606 | 607 | 0.026708 | 0.040310 | 0.026640 | 0.008587 | Vegetable | 4 | High | 1 |
| 607 | 608 | 0.017792 | 0.145157 | 0.038666 | 0.018081 | Pork | 4 | High | 1 |
| 610 | 611 | 0.066300 | 0.147552 | 0.003045 | 0.004458 | Potato | 1 | High | 1 |
| 612 | 613 | 0.268539 | 0.013198 | 0.015756 | 0.027328 | Lunch/Snacks | 1 | High | 1 |
| 614 | 615 | 0.080568 | 0.018006 | 0.076039 | 0.007926 | Potato | 4 | High | 1 |
| 616 | 617 | 0.131558 | 0.118630 | 0.150708 | 0.495542 | Pork | 4 | High | -1 |
| 620 | 621 | 0.008947 | 0.091254 | 0.014995 | 0.083884 | Lunch/Snacks | 4 | High | 1 |
| 621 | 622 | 0.150908 | 0.240276 | 0.027401 | 0.000193 | Dessert | 2 | High | 1 |
| 622 | 623 | 0.045350 | 0.014914 | 0.018344 | 0.009797 | Vegetable | 1 | High | 1 |
| 623 | 624 | 0.081380 | 0.018175 | 0.006089 | 0.024961 | Potato | 6 | High | 1 |
| 625 | 626 | 0.038880 | 0.165030 | 0.129015 | 0.004266 | Breakfast | 2 | High | 1 |
| 626 | 627 | 0.187163 | 0.074021 | 0.013244 | 0.006330 | Chicken Breast | 6 | High | 1 |
| 628 | 629 | 0.249251 | 0.009710 | 0.015908 | 0.029365 | One Dish Meal | 6 | High | 1 |
| 630 | 631 | 0.134297 | 0.095156 | 0.013929 | 0.089883 | Lunch/Snacks | 1 | High | 1 |
| 631 | 632 | 0.103129 | 0.076849 | 0.033110 | 0.002917 | Chicken Breast | 6 | High | 1 |
| 632 | 633 | 0.036413 | 0.105149 | 0.000457 | 0.020778 | Vegetable | 2 | High | 1 |
| 633 | 634 | 0.186423 | 0.100605 | 0.008297 | 0.023145 | Vegetable | 4 | High | 1 |
| 635 | 636 | 0.175731 | 0.091932 | 0.014157 | 0.019870 | Lunch/Snacks | 1 | High | 1 |
| 636 | 637 | 0.698307 | 0.047229 | 0.004567 | 0.148613 | One Dish Meal | 1 | High | -1 |
| 638 | 639 | 0.110745 | 0.002602 | 0.081824 | 0.309996 | Meat | 1 | High | 1 |
| 640 | 641 | 0.047662 | 0.033485 | 0.064774 | 0.018769 | Vegetable | 2 | High | 1 |
| 642 | 643 | 0.554023 | 0.080224 | 0.001751 | 0.133587 | Meat | 4 | High | -1 |
| 644 | 645 | 0.008954 | 0.061653 | 0.009971 | 0.002009 | Potato | 2 | High | 1 |
| 645 | 646 | 0.444383 | 0.119893 | 0.062490 | 0.051904 | Lunch/Snacks | 1 | High | 1 |
| 647 | 648 | 0.186664 | 0.014122 | 0.059446 | 0.112120 | Pork | 4 | High | 1 |
| 650 | 651 | 0.092681 | 0.132921 | 0.049551 | 0.063986 | Potato | 4 | High | 1 |
| 652 | 653 | 0.000138 | 0.057618 | 0.079464 | 0.000991 | Beverages | 6 | High | 1 |
| 653 | 654 | 0.252186 | 0.015611 | 0.006850 | 0.001871 | Potato | 6 | High | 1 |
| 654 | 655 | 0.000155 | 0.035427 | 0.441391 | 0.045244 | Dessert | 2 | High | -1 |
| 655 | 656 | 0.100400 | 0.068346 | 0.480515 | 0.019320 | Dessert | 4 | High | -1 |
| 657 | 658 | 0.046434 | 0.049567 | 0.034785 | 0.019567 | Potato | 4 | High | 1 |
| 658 | 659 | 0.005757 | 0.116405 | 0.044908 | 0.028402 | Breakfast | 1 | High | 1 |
| 659 | 660 | 0.129204 | 0.029016 | 0.057771 | 0.048712 | One Dish Meal | 4 | High | 1 |
| 660 | 661 | 0.099358 | 0.053923 | 0.386665 | 0.021219 | Dessert | 6 | High | 1 |
| 661 | 662 | 0.248349 | 0.000302 | 0.002436 | 0.052290 | Dessert | 1 | High | 1 |
| 665 | 666 | 0.221527 | 0.001263 | 0.271959 | 0.035667 | Vegetable | 6 | High | -1 |
| 666 | 667 | 0.034499 | 0.018156 | 0.185416 | 0.046703 | Vegetable | 2 | High | 1 |
| 667 | 668 | 0.437652 | 0.094459 | 0.127797 | 0.053501 | Pork | 2 | High | 1 |
| 668 | 669 | 0.118319 | 0.044797 | 0.448090 | 0.001128 | Dessert | 1 | High | -1 |
| 670 | 671 | 0.165782 | 0.023963 | 0.010123 | 0.142834 | Chicken | 1 | High | 1 |
| 672 | 673 | 0.022675 | 0.042308 | 0.037525 | 0.004899 | Vegetable | 2 | High | 1 |
| 673 | 674 | 0.005386 | 0.022361 | 0.038590 | 0.010320 | Vegetable | 4 | High | 1 |
| 674 | 675 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 4 | High | 1 |
| 675 | 676 | 0.246639 | 0.028451 | 0.031664 | 0.006192 | Potato | 6 | High | 1 |
| 678 | 679 | 0.053636 | 0.039462 | 0.078170 | 0.212984 | Meat | 1 | High | 1 |
| 679 | 680 | 0.061534 | 0.042535 | 0.030674 | 0.019375 | One Dish Meal | 2 | High | 1 |
| 681 | 682 | 0.116743 | 0.010785 | 0.077485 | 1.000000 | Chicken Breast | 6 | High | -1 |
| 683 | 684 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Potato | 1 | High | 1 |
| 685 | 686 | 0.025177 | 0.014518 | 0.029989 | 0.100369 | Vegetable | 6 | High | 1 |
| 687 | 688 | 0.085754 | 0.130828 | 0.060131 | 0.031071 | Dessert | 4 | High | 1 |
| 688 | 689 | 0.133925 | 0.260224 | 0.059370 | 0.021852 | Potato | 6 | High | 1 |
| 689 | 690 | 0.166735 | 0.114840 | 0.007916 | 0.012247 | Potato | 4 | High | 1 |
| 690 | 691 | 0.095741 | 0.041894 | 0.011417 | 0.049262 | Chicken | 1 | High | 1 |
| 691 | 692 | 0.019536 | 0.201908 | 0.014462 | 0.002202 | Vegetable | 4 | High | 1 |
| 693 | 694 | 0.011281 | 0.322121 | 0.396103 | 0.017256 | Dessert | 4 | High | -1 |
| 694 | 695 | 0.463978 | 0.286978 | 0.001066 | 0.007045 | One Dish Meal | 2 | High | -1 |
| 696 | 697 | 0.583189 | 0.078263 | 0.083346 | 0.090048 | Meat | 4 | High | -1 |
| 698 | 699 | 0.029303 | 0.087407 | 0.021084 | 0.013981 | Vegetable | 2 | High | 1 |
| 699 | 700 | 0.216235 | 0.039273 | 0.002436 | 0.048822 | Chicken Breast | 6 | High | 1 |
| 700 | 701 | 0.038653 | 0.029243 | 0.032882 | 0.071830 | Lunch/Snacks | 4 | High | 1 |
| 702 | 703 | 0.179354 | 0.009144 | 0.088902 | 0.090764 | Chicken | 6 | High | 1 |
| 707 | 708 | 0.102905 | 0.038877 | 0.048181 | 0.034814 | Pork | 6 | High | 1 |
| 709 | 710 | 0.111068 | 0.027961 | 0.000076 | 0.018824 | Lunch/Snacks | 6 | High | 1 |
| 710 | 711 | 0.071383 | 0.002809 | 0.033871 | 0.041419 | One Dish Meal | 6 | High | 1 |
| 711 | 712 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Lunch/Snacks | 4 | High | 1 |
| 712 | 713 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 6 | High | 1 |
| 713 | 714 | 0.247833 | 0.012915 | 0.005709 | 0.047446 | Meat | 4 | High | 1 |
| 714 | 715 | 0.359311 | 0.058146 | 0.028086 | 0.014779 | Potato | 4 | High | 1 |
| 716 | 717 | 0.038966 | 0.009182 | 0.009743 | 0.047006 | Pork | 6 | High | 1 |
| 717 | 718 | 0.343942 | 0.264315 | 0.038590 | 0.002092 | One Dish Meal | 1 | High | 1 |
| 718 | 719 | 0.314883 | 0.084410 | 0.068199 | 0.039272 | Potato | 4 | High | 1 |
| 719 | 720 | 0.146466 | 0.305586 | 0.004795 | 0.021274 | Lunch/Snacks | 6 | High | 1 |
| 720 | 721 | 0.082234 | 0.152341 | 0.002207 | 0.063986 | Breakfast | 2 | High | 1 |
| 721 | 722 | 0.546521 | 0.127849 | 0.102146 | 0.016072 | Dessert | 6 | High | -1 |
| 722 | 723 | 0.368189 | 0.234035 | 0.016517 | 0.139916 | One Dish Meal | 6 | High | -1 |
| 723 | 724 | 0.335345 | 0.004977 | 0.035850 | 0.008476 | Pork | 4 | High | 1 |
| 724 | 725 | 0.090782 | 0.001791 | 0.056325 | 0.035997 | Pork | 4 | High | 1 |
| 725 | 726 | 0.009402 | 0.015592 | 0.052519 | 0.049950 | Meat | 4 | High | 1 |
| 726 | 727 | 0.137350 | 0.182281 | 0.065383 | 0.143109 | Pork | 4 | High | 1 |
| 728 | 729 | 0.281633 | 0.009314 | 0.009438 | 0.086939 | Lunch/Snacks | 4 | High | 1 |
| 731 | 732 | 0.245066 | 0.043873 | 0.063784 | 0.150017 | Chicken | 2 | High | 1 |
| 732 | 733 | 0.422593 | 0.061295 | 0.012635 | 0.020310 | Potato | 4 | High | 1 |
| 733 | 734 | 0.073950 | 0.016648 | 0.008297 | 0.010375 | Chicken Breast | 1 | High | 1 |
| 734 | 735 | 0.018669 | 0.116405 | 0.066677 | 0.035474 | Pork | 4 | High | 1 |
| 737 | 738 | 0.236315 | 0.033353 | 0.728726 | 0.004513 | Dessert | 4 | High | -1 |
| 738 | 739 | 0.061634 | 0.499745 | 0.001066 | 0.124642 | One Dish Meal | 6 | High | -1 |
| 739 | 740 | 0.030438 | 0.082373 | 0.029609 | 0.084627 | Lunch/Snacks | 4 | High | 1 |
| 740 | 741 | 0.482764 | 0.003959 | 0.030446 | 0.054354 | Chicken Breast | 6 | High | 1 |
| 741 | 742 | 0.031615 | 0.096646 | 0.073451 | 0.005367 | Dessert | 4 | High | 1 |
| 742 | 743 | 0.359655 | 0.139445 | 0.003958 | 0.096158 | Meat | 1 | High | 1 |
| 743 | 744 | 0.004422 | 0.129659 | 0.027630 | 0.031319 | Lunch/Snacks | 1 | High | 1 |
| 744 | 745 | 0.090472 | 0.099625 | 0.009591 | 0.053748 | Potato | 6 | High | 1 |
| 745 | 746 | 0.044369 | 0.070269 | 0.015832 | 0.086884 | Pork | 2 | High | 1 |
| 749 | 750 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Dessert | 4 | High | 1 |
| 752 | 753 | 0.474030 | 0.035747 | 0.060283 | 0.107166 | One Dish Meal | 4 | High | 1 |
| 753 | 754 | 0.453365 | 0.045985 | 0.200563 | 0.236102 | Pork | 4 | High | -1 |
| 754 | 755 | 0.023934 | 0.143404 | 0.013016 | 0.272815 | One Dish Meal | 2 | High | -1 |
| 755 | 756 | 0.271888 | 0.007334 | 0.045060 | 0.063738 | Chicken Breast | 4 | High | 1 |
| 759 | 760 | 0.302340 | 0.014461 | 0.029380 | 0.096653 | Pork | 6 | High | 1 |
| 761 | 762 | 0.067997 | 0.422576 | 0.022530 | 0.003468 | Potato | 2 | High | -1 |
| 762 | 763 | 0.128915 | 0.006392 | 0.137464 | 0.069876 | One Dish Meal | 1 | High | 1 |
| 763 | 764 | 0.087970 | 0.209374 | 0.109986 | 0.005972 | Pork | 1 | High | 1 |
| 765 | 766 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 1 | High | 1 |
| 766 | 767 | 0.074897 | 0.001112 | 0.034252 | 0.001266 | Vegetable | 6 | High | 1 |
| 767 | 768 | 0.116010 | 0.060126 | 0.060816 | 0.075710 | One Dish Meal | 1 | High | 1 |
| 770 | 771 | 0.309508 | 0.104659 | 0.058076 | 0.050253 | Dessert | 2 | High | 1 |
| 773 | 774 | 0.205656 | 0.065499 | 0.035698 | 0.105295 | Chicken Breast | 1 | High | 1 |
| 775 | 776 | 0.437157 | 0.020193 | 0.016593 | 0.143081 | Pork | 4 | High | 1 |
| 778 | 779 | 0.082987 | 0.007956 | 0.058456 | 0.027163 | Lunch/Snacks | 6 | High | 1 |
| 779 | 780 | 0.004167 | 0.021003 | 0.056554 | 0.074004 | Meat | 6 | High | 1 |
| 781 | 782 | 0.711639 | 0.064481 | 0.011037 | 0.027576 | Potato | 1 | High | -1 |
| 782 | 783 | 0.015145 | 0.035050 | 0.012483 | 0.502614 | Pork | 1 | High | -1 |
| 783 | 784 | 0.182087 | 0.014159 | 0.052748 | 0.069380 | Meat | 4 | High | 1 |
| 785 | 786 | 0.008156 | 0.102717 | 0.004719 | 0.001624 | Breakfast | 6 | High | 1 |
| 787 | 788 | 0.034651 | 0.307095 | 0.003045 | 0.015632 | Potato | 6 | High | 1 |
| 788 | 789 | 0.024629 | 0.097739 | 0.074517 | 0.041557 | Meat | 4 | High | 1 |
| 789 | 790 | 0.012217 | 0.174230 | 0.120414 | 0.028264 | One Dish Meal | 4 | High | 1 |
| 792 | 793 | 0.224263 | 0.119968 | 0.039884 | 0.143604 | One Dish Meal | 6 | High | 1 |
| 793 | 794 | 0.217845 | 0.037652 | 0.017811 | 0.071857 | Lunch/Snacks | 4 | High | 1 |
| 794 | 795 | 0.165654 | 0.011237 | 0.041254 | 0.078462 | Meat | 4 | High | 1 |
| 795 | 796 | 0.014378 | 0.047606 | 0.014766 | 0.040236 | Chicken Breast | 2 | High | 1 |
| 796 | 797 | 0.228968 | 0.029261 | 0.005785 | 0.051739 | Chicken | 4 | High | 1 |
| 797 | 798 | 0.032452 | 0.025227 | 0.018800 | 0.008917 | Potato | 4 | High | 1 |
| 798 | 799 | 0.034647 | 0.193084 | 0.009438 | 0.092223 | Potato | 1 | High | 1 |
| 801 | 802 | 0.371039 | 0.025359 | 0.086847 | 0.376073 | Pork | 6 | High | -1 |
| 803 | 804 | 0.343783 | 0.096740 | 0.021388 | 0.069490 | Lunch/Snacks | 1 | High | 1 |
| 806 | 807 | 0.033157 | 0.070420 | 0.029837 | 0.003413 | Potato | 6 | High | 1 |
| 807 | 808 | 0.025266 | 0.029375 | 0.044908 | 0.012962 | Lunch/Snacks | 6 | High | 1 |
| 811 | 812 | 0.362490 | 0.012180 | 0.080073 | 0.028264 | Vegetable | 4 | High | 1 |
| 812 | 813 | 0.189468 | 0.021682 | 0.002588 | 0.000000 | Potato | 2 | High | 1 |
| 813 | 814 | 0.428636 | 0.000094 | 0.109073 | 0.009687 | Lunch/Snacks | 4 | High | 1 |
| 816 | 817 | 0.184251 | 0.000094 | 0.033415 | 0.015302 | One Dish Meal | 6 | High | 1 |
| 818 | 819 | 0.071638 | 0.158713 | 0.037905 | 0.119716 | One Dish Meal | 6 | High | 1 |
| 819 | 820 | 0.206513 | 0.096250 | 0.137464 | 0.016265 | Dessert | 4 | High | 1 |
| 820 | 821 | 0.002578 | 0.073455 | 0.039580 | 0.001761 | Vegetable | 4 | High | 1 |
| 821 | 822 | 0.151063 | 0.072437 | 0.020627 | 0.043208 | Pork | 1 | High | 1 |
| 822 | 823 | 0.072959 | 0.046871 | 0.012559 | 0.016870 | One Dish Meal | 2 | High | 1 |
| 823 | 824 | 0.466748 | 0.016158 | 0.025270 | 0.006137 | Pork | 2 | High | 1 |
| 824 | 825 | 0.085258 | 0.084183 | 0.020018 | 0.054877 | Chicken Breast | 1 | High | 1 |
| 825 | 826 | 0.004164 | 0.069949 | 0.025727 | 0.000771 | Vegetable | 1 | High | 1 |
| 826 | 827 | 0.078589 | 0.076057 | 0.039351 | 0.012522 | Potato | 1 | High | 1 |
| 827 | 828 | 0.024502 | 0.072098 | 0.021845 | 0.068087 | Meat | 1 | High | 1 |
| 828 | 829 | 0.142608 | 0.033579 | 0.009286 | 0.020641 | Potato | 6 | High | 1 |
| 829 | 830 | 0.314064 | 0.150663 | 0.014005 | 0.122083 | One Dish Meal | 1 | High | 1 |
| 830 | 831 | 0.218059 | 0.431079 | 0.008297 | 0.011394 | Potato | 4 | High | -1 |
| 831 | 832 | 0.232154 | 0.040442 | 0.029837 | 0.065665 | Lunch/Snacks | 1 | High | 1 |
| 832 | 833 | 0.008080 | 0.093874 | 0.037753 | 0.015219 | Vegetable | 4 | High | 1 |
| 833 | 834 | 0.029554 | 0.059315 | 0.009591 | 0.018494 | Lunch/Snacks | 6 | High | 1 |
| 834 | 835 | 0.037307 | 0.080262 | 0.007840 | 0.005394 | Vegetable | 4 | High | 1 |
| 837 | 838 | 0.004298 | 0.076906 | 0.004415 | 0.009385 | Vegetable | 4 | High | 1 |
| 838 | 839 | 0.250324 | 0.005241 | 0.007612 | 0.078572 | Meat | 4 | High | 1 |
| 839 | 840 | 0.117063 | 0.000716 | 0.017354 | 0.029035 | Chicken | 4 | High | 1 |
| 840 | 841 | 0.029172 | 0.053319 | 0.075278 | 0.114294 | Chicken | 6 | High | 1 |
| 842 | 843 | 0.097303 | 0.216105 | 0.019029 | 0.077361 | Lunch/Snacks | 6 | High | 1 |
| 843 | 844 | 0.088022 | 0.297687 | 0.016517 | 0.009550 | Potato | 4 | High | 1 |
| 844 | 845 | 0.277015 | 0.366202 | 0.349673 | 0.011091 | Dessert | 4 | High | -1 |
| 848 | 849 | 0.022028 | 0.011520 | 0.001979 | 0.080719 | Meat | 2 | High | 1 |
| 851 | 852 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Lunch/Snacks | 4 | High | 1 |
| 855 | 856 | 0.049338 | 0.011576 | 0.004034 | 0.013760 | Pork | 4 | High | 1 |
| 856 | 857 | 0.124796 | 0.183224 | 0.004110 | 0.058097 | Lunch/Snacks | 2 | High | 1 |
| 857 | 858 | 0.038945 | 0.021626 | 0.013092 | 0.031924 | Vegetable | 2 | High | 1 |
| 858 | 859 | 0.133626 | 0.024246 | 0.035089 | 0.038337 | Pork | 4 | High | 1 |
| 861 | 862 | 0.566622 | 0.017704 | 0.038895 | 0.675831 | Chicken Breast | 6 | High | -1 |
| 864 | 865 | 0.019784 | 0.014631 | 0.037601 | 0.010320 | Vegetable | 4 | High | 1 |
| 865 | 866 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Lunch/Snacks | 6 | High | 1 |
| 868 | 869 | 0.103704 | 0.019533 | 0.106637 | 0.013595 | Lunch/Snacks | 4 | High | 1 |
| 870 | 871 | 0.506282 | 0.163050 | 0.005937 | 0.001596 | Potato | 1 | High | -1 |
| 871 | 872 | 0.152109 | 0.010539 | 0.005937 | 0.164272 | One Dish Meal | 2 | High | 1 |
| 872 | 873 | 0.079195 | 0.017402 | 0.003197 | 0.156319 | One Dish Meal | 6 | High | 1 |
| 873 | 874 | 0.055460 | 0.066479 | 0.024509 | 0.009660 | Potato | 6 | High | 1 |
| 874 | 875 | 0.336123 | 0.014518 | 0.022835 | 0.134963 | One Dish Meal | 2 | High | 1 |
| 875 | 876 | 0.048701 | 0.072588 | 0.022454 | 0.027879 | Potato | 4 | High | 1 |
| 876 | 877 | 0.008627 | 0.000830 | 0.157025 | 0.144650 | Pork | 2 | High | -1 |
| 878 | 879 | 0.030325 | 0.034974 | 0.039580 | 0.274218 | Meat | 6 | High | 1 |
| 881 | 882 | 0.075403 | 0.005958 | 0.044223 | 0.035089 | Vegetable | 4 | High | 1 |
| 886 | 887 | 0.010403 | 0.072343 | 0.000685 | 0.096874 | Vegetable | 2 | High | 1 |
| 887 | 888 | 0.590491 | 0.037633 | 0.050997 | 0.069600 | Chicken Breast | 2 | High | 1 |
| 888 | 889 | 0.388548 | 0.160863 | 0.012787 | 0.032805 | Pork | 4 | High | 1 |
| 890 | 891 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Meat | 4 | High | 1 |
| 892 | 893 | 0.046179 | 0.044194 | 0.033871 | 0.063876 | Vegetable | 4 | High | 1 |
| 895 | 896 | 0.106306 | 0.237844 | 0.287334 | 0.007596 | Dessert | 4 | High | -1 |
| 896 | 897 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Chicken | 6 | High | 1 |
| 898 | 899 | 0.221569 | 0.059447 | 0.017430 | 0.157255 | Meat | 2 | High | 1 |
| 900 | 901 | 0.040108 | 0.037538 | 0.074136 | 0.008284 | Vegetable | 2 | High | 1 |
| 901 | 902 | 0.031822 | 0.002489 | 0.027249 | 0.000110 | Beverages | 6 | High | 1 |
| 902 | 903 | 0.268278 | 0.046928 | 0.012255 | 0.061784 | Pork | 4 | High | 1 |
| 903 | 904 | 0.097888 | 0.018288 | 0.013320 | 0.307051 | Meat | 4 | High | 1 |
| 904 | 905 | 0.154542 | 0.181150 | 0.034937 | 0.018109 | Pork | 2 | High | 1 |
| 907 | 908 | 0.802575 | 0.014027 | 0.027478 | 0.094479 | One Dish Meal | 1 | High | -1 |
| 908 | 909 | 0.037607 | 0.097362 | 0.019257 | 0.012384 | Lunch/Snacks | 1 | High | 1 |
| 909 | 910 | 0.298919 | 0.087558 | 0.020323 | 0.063436 | Lunch/Snacks | 1 | High | 1 |
| 910 | 911 | 0.025669 | 0.005581 | 0.008905 | 0.000605 | Potato | 6 | High | 1 |
| 911 | 912 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Dessert | 6 | High | 1 |
| 914 | 915 | 0.079567 | 0.114124 | 0.000000 | 0.014201 | Potato | 6 | High | 1 |
| 915 | 916 | 0.042855 | 0.042045 | 0.004567 | 0.003605 | Potato | 4 | High | 1 |
| 918 | 919 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 6 | High | 1 |
| 919 | 920 | 0.395204 | 0.019495 | 0.043309 | 0.084847 | Meat | 6 | High | 1 |
| 920 | 921 | 0.129111 | 0.248440 | 0.124981 | 0.013100 | Chicken | 4 | High | 1 |
| 922 | 923 | 0.009467 | 0.058655 | 0.027325 | 0.023833 | Potato | 4 | High | 1 |
| 923 | 924 | 0.111492 | 0.053527 | 0.059218 | 0.034319 | Meat | 2 | High | 1 |
| 926 | 927 | 0.092606 | 0.056920 | 0.103669 | 0.101057 | Meat | 2 | High | 1 |
| 927 | 928 | 0.087065 | 0.036200 | 0.085325 | 0.006467 | One Dish Meal | 2 | High | 1 |
| 928 | 929 | 0.001972 | 0.090273 | 0.228726 | 0.068032 | Meat | 4 | High | 1 |
| 930 | 931 | 0.209018 | 0.065706 | 0.041711 | 0.026943 | Lunch/Snacks | 4 | High | 1 |
| 931 | 932 | 0.037865 | 0.123815 | 0.442533 | 0.016292 | Dessert | 1 | High | -1 |
| 933 | 934 | 0.016381 | 0.009993 | 0.006546 | 0.047941 | Chicken Breast | 4 | High | 1 |
| 934 | 935 | 0.039052 | 0.013368 | 0.040037 | 0.036878 | Vegetable | 4 | High | 1 |
| 935 | 936 | 0.090214 | 0.113313 | 0.551682 | 0.013953 | Dessert | 4 | High | -1 |
| 936 | 937 | 0.056685 | 0.002998 | 0.057543 | 0.133064 | Pork | 4 | High | 1 |
| 937 | 938 | 0.043419 | 0.085692 | 0.047572 | 0.028732 | Dessert | 1 | High | 1 |
| 938 | 939 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Pork | 4 | High | 1 |
| 941 | 942 | 0.064032 | 0.158204 | 0.014995 | 0.061647 | Chicken Breast | 4 | High | 1 |
| 943 | 944 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | Potato | 2 | High | 1 |
| 944 | 945 | 0.327475 | 0.055412 | 0.027097 | 0.038172 | Pork | 2 | High | 1 |
| 945 | 946 | 0.091701 | 0.067384 | 0.007307 | 0.022209 | Potato | 6 | High | 1 |
# Set the background color to black
plt.style.use('dark_background')
# Create a figure with a specified size
plt.figure(figsize=(12, 8))
# Scatter plot of original vs. outlier status
sns.scatterplot(data=df_scaled, x='calories', y='protein', hue='outlier',
palette={1: '#F0F8FF', -1: '#FFE4E1'}) # Use specified colors
# Set the title of the plot
plt.title('Outliers Detection Using Isolation Forest', fontsize=18, weight='bold', color='white')
# Set the x-axis label
plt.xlabel('Calories', fontsize=14, color='white')
# Set the y-axis label
plt.ylabel('Protein', fontsize=14, color='white')
# Set the legend title and location
plt.legend(title='Outlier Status', loc='upper right', fontsize=12, facecolor='#F0F8FF')
# Show the plot
plt.show()
🌟 Exploratory Data Analysis (EDA) Summary 🌟¶
1. Dataset Overview¶
We embarked on our analytical journey by surveying the landscape of our dataset understanding its dimensions, with the number of rows and columns setting the stage for our exploration.
2. Statistical Snapshot 📈¶
Dived deep into the numbers to unveil essential metrics like the mean, median, and standard deviation. This statistical deep-dive was crucial in mapping out the data's distribution and variability, giving us a clear picture of what to expect.
3. Data Type Insights 🧐¶
Peered into the data's soul by categorizing variable types. This step was key to shaping our approach, allowing us to apply tailored processing strategies to each data type.
4. Null Values Handling 🚫¶
Tackled missing data with a well-orchestrated strategy:
Initial Imputation: Filled the gaps with column means, ensuring our dataset remained intact and analysis-ready.
Log Transformation: Transformed certain columns using a logarithmic approach to smooth out skewness, normalize the data, and stabilize variance.
Re-Transformation and Cleaning: Post-transformation, re-imputed remaining null values to preserve data integrity. Reversed the log transformation to return to the original data scale, tidying up intermediate columns used during this process.
5. Duplicate Handling 🔍¶
Safeguarded data purity by rooting out duplicates. This meticulous step was pivotal in maintaining unique data points and ensuring our analysis was based on clean, non-redundant information.
6. Data Visualization 🎨¶
Brought the data to life with a suite of visualizations to reveal hidden patterns and insights:
Numeric Columns: Explored distributions and trends with histograms and box plots, illuminating the spread and central tendencies of our numerical features.
Categorical Columns: Unveiled the distribution of categorical variables through bar charts and count plots, shedding light on the frequency and spread of categories.
Relationships:
Correlation Matrix: Mapped out the interrelationships between numeric features to identify dependencies and correlations.
Scatter Plots: Illustrated pairwise relationships, uncovering both linear and non-linear correlations between variables.
Box Plots: Highlighted distribution ranges and potential outliers, offering a clear view of data dispersion.
Pair Plots: Provided a panoramic view of relationships across all numeric features, offering a holistic perspective.
Count Plots: Showcased the frequency of categorical variable occurrences, emphasizing distribution trends.
7. Skewness Handling 📉¶
Addressed skewness to bring balance to our data:
Skewness Calculation: Measured skewness before and after applying transformations to gauge data normalization.
Transformations:
Box-Cox Transformation: Applied to stabilize variance and reduce skewness for positive values.
Log Transformation: Utilized for handling zeros and reducing skewness in non-positive values.
Styled Tables: Created visually appealing tables to compare skewness pre- and post-transformation, showcasing the effectiveness of our adjustments.
8. Outlier Handling 🚀¶
Handled outliers with precision using the Isolation Forest algorithm:
Outlier Detection: Implemented Isolation Forest to pinpoint anomalies. Outliers were tagged for further scrutiny.
Visualization: Displayed outliers on scatter plots to visually distinguish them from the primary data, enhancing our understanding of their impact.
This summary reflects the meticulous and creative approach taken during the EDA process, providing a vivid and engaging overview of data cleaning, transformation, and visualization techniques used to prepare the dataset for advanced analysis and modeling.
3 | Feature Engineering
3.1 | Categorical Features Handling
# Initialize the label encoder
label_encoder = LabelEncoder()
# Encode the 'category' column
df['category'] = label_encoder.fit_transform(df['category'])
# Encode the 'high_traffic' column
df['high_traffic'] = label_encoder.fit_transform(df['high_traffic'])
3.2 | Scaling Features
df1 = df.copy()
# Initialize the scaler
scaler = MinMaxScaler()
# Define features to scale (include newly encoded columns if necessary)
features_to_scale = ['calories', 'carbohydrate', 'sugar', 'protein', 'recipe']
# Apply scaling
df1[features_to_scale] = scaler.fit_transform(df1[features_to_scale])
df1.head()
| recipe | calories | carbohydrate | sugar | protein | category | servings | high_traffic | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 0.099251 | 0.040442 | 0.034556 | 0.029723 | 8 | 6 | 0 |
| 1 | 0.001058 | 0.012162 | 0.072645 | 0.004947 | 0.002532 | 9 | 4 | 0 |
| 3 | 0.003175 | 0.033343 | 0.057561 | 0.293956 | 0.000055 | 0 | 4 | 0 |
| 5 | 0.005291 | 0.237798 | 0.006467 | 0.012483 | 0.148420 | 7 | 2 | 0 |
| 8 | 0.008466 | 0.185273 | 0.007070 | 0.025575 | 0.010430 | 8 | 6 | 0 |
4 | Data Separation
# Define features (X) and target (y)
X = df1.drop(columns=['high_traffic'])
y = df1['high_traffic']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
# Display the shapes of the resulting datasets
print(f"Training set shape: {X_train.shape}")
print(f"Testing set shape: {X_test.shape}")
Training set shape: (430, 7) Testing set shape: (144, 7)
5 | Models Building & Evaluation
Problem Type: Classification¶
The task at hand is a classification problem where we aim to predict whether a recipe will drive high traffic ("High") or not. This involves distinguishing between two classes based on various features associated with each recipe.
Models Chosen:¶
1.Decision Tree Classifier¶
2. Random Forest Classifier¶
Model # 1: Random Forest Classifier
Reasons for Selection:¶
Enhanced Accuracy:¶
Improves upon Decision Trees by aggregating predictions from multiple trees, leading to better accuracy and reduced overfitting.
Complex Pattern Recognition:¶
Handles intricate patterns and interactions within the data, modeling complex relationships between features and traffic.
Stability and Robustness:¶
Offers greater stability and robustness by mitigating the impact of outliers and noise, ensuring reliable predictions.
Feature Importance:¶
Provides a comprehensive view of feature importance by averaging scores across trees, highlighting which recipe attributes are most impactful.
# Initialize the Random Forest model
rf = RandomForestClassifier(random_state=42)
# Set up parameters for Grid Search
param_grid = {
'n_estimators': [50, 100, 150], # Number of trees
'max_depth': [None, 10, 20, 30], # Depth of each tree
'min_samples_split': [2, 5, 10], # Minimum samples required to split an internal node
'min_samples_leaf': [1, 2, 4], # Minimum samples required to be at a leaf node
'max_features': ['auto', 'sqrt', 'log2'] # Number of features to consider for the best split
}
# Perform Grid Search with Cross-Validation
grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=2)
grid_search.fit(X_train, y_train)
# Best parameters from Grid Search
print("Best Parameters:", grid_search.best_params_)
# Train the model with the best parameters
best_rf = grid_search.best_estimator_
best_rf.fit(X_train, y_train)
Fitting 5 folds for each of 324 candidates, totalling 1620 fits
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time= 0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.1s
Best Parameters: {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}
RandomForestClassifier(max_features='auto', n_estimators=50, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier(max_features='auto', n_estimators=50, random_state=42)
# Predict on the test set
y_pred = best_rf.predict(X_test)
# Detailed Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))
# Cross-Validation Score
cv_scores = cross_val_score(best_rf, X, y, cv=5, scoring='accuracy')
print(f"Cross-Validation Accuracy Scores: {cv_scores}")
print(f"Mean Cross-Validation Accuracy: {cv_scores.mean():.2f}")
Accuracy: 1.0
Classification Report:
precision recall f1-score support
0 1.00 1.00 1.00 144
accuracy 1.00 144
macro avg 1.00 1.00 1.00 144
weighted avg 1.00 1.00 1.00 144
Cross-Validation Accuracy Scores: [1. 1. 1. 1. 1.]
Mean Cross-Validation Accuracy: 1.00
# Create DataFrame for actual vs. predicted values
results_df = pd.DataFrame({
'Actual': y_test.reset_index(drop=True),
'Predicted': y_pred
})
# Calculate percentage of correct predictions (assuming a margin of ±10)
correct_predictions = sum(abs(results_df['Actual'] - results_df['Predicted']) <= 10)
total_predictions = len(results_df)
correct_percentage = (correct_predictions / total_predictions) * 100
# Function to style the DataFrame
def apply_styles():
"""Apply consistent background color with customized styling."""
styles = {
'selector': 'th',
'props': [('background-color', '#000000'), ('color', 'white')]
}
return [styles]
# Function to apply the desired background color to all cells
def highlight_background(val):
"""Apply a special color to all cells."""
return 'background-color: #eac086; color: black'
# Apply the styling to the DataFrame
styled_results_df = results_df.style.applymap(highlight_background)\
.set_table_styles(apply_styles())\
.set_caption(f"Actual vs. Predicted Values (Correct: {correct_percentage:.2f}%)")
# Display the styled DataFrame
styled_results_df
| Actual | Predicted | |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 0 | 0 |
| 2 | 0 | 0 |
| 3 | 0 | 0 |
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Model # 2: Decision Tree Classifier
Reasons for Selection:¶
Clarity and Transparency:¶
Visualizes decisions through a tree structure, making it easy to understand how predictions are made based on recipe features.
Handling Non-Linearity:¶
Captures complex, non-linear relationships between features, useful for modeling intricate interactions affecting recipe traffic.
Feature Insights:¶
Provides direct insights into which features most influence high traffic, helping to identify key attributes driving user engagement.
# Initialize the Decision Tree Classifier
tree_classifier = DecisionTreeClassifier(random_state=42)
# Fit the model
tree_classifier.fit(X_train, y_train)
DecisionTreeClassifier(random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(random_state=42)
# Predict on the test set
y_pred_dt = tree_classifier.predict(X_test)
# Detailed Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred_dt))
Accuracy: 1.0
# Create DataFrame for actual vs. predicted values
results_df = pd.DataFrame({
'Actual': y_test.reset_index(drop=True),
'Predicted': y_pred_dt
})
# Calculate percentage of correct predictions (assuming a margin of ±10)
correct_predictions = sum(abs(results_df['Actual'] - results_df['Predicted']) <= 10)
total_predictions = len(results_df)
correct_percentage = (correct_predictions / total_predictions) * 100
# Function to style the DataFrame
def apply_styles():
"""Apply consistent background color with customized styling."""
styles = {
'selector': 'th',
'props': [('background-color', '#FFE4E1'), ('color', 'black')]
}
return [styles]
# Function to apply the desired background color to all cells
def highlight_background(val):
"""Apply a special color to all cells."""
return 'background-color: #F0F8FF; color: black'
# Apply the styling to the DataFrame
styled_results_df = results_df.style.applymap(highlight_background)\
.set_table_styles(apply_styles())\
.set_caption(f"Actual vs. Predicted Values (Correct: {correct_percentage:.2f}%)")
# Display the styled DataFrame
styled_results_df
| Actual | Predicted | |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 0 | 0 |
| 2 | 0 | 0 |
| 3 | 0 | 0 |
| 4 | 0 | 0 |
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| 135 | 0 | 0 |
| 136 | 0 | 0 |
| 137 | 0 | 0 |
| 138 | 0 | 0 |
| 139 | 0 | 0 |
| 140 | 0 | 0 |
| 141 | 0 | 0 |
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| 143 | 0 | 0 |
6 | Models Comparison
🏆 Model Comparison: Why Random Forest Triumphs Over Decision Tree 🏆¶
🎯 Accuracy Boost¶
- Random Forest: Aggregates predictions from multiple Decision Trees to enhance overall accuracy and minimize overfitting. 🌟
- Decision Tree: Provides initial insights but can struggle with accuracy and overfitting issues. ❌
🔍 Complex Pattern Detection¶
- Random Forest: Expertly captures intricate relationships between features by combining diverse tree predictions. 🌐
- Decision Tree: Limited to simpler patterns and may miss complex interactions. 🚧
🚀 Stability & Robustness¶
- Random Forest: Delivers greater stability by averaging predictions, reducing the impact of outliers and noise. 🛡️
- Decision Tree: More prone to instability and overfitting due to reliance on a single tree. ⚠️
🏅 Detailed Feature Importance¶
- Random Forest: Offers a comprehensive view of feature importance across multiple trees, revealing key drivers of high traffic. 🔍
- Decision Tree: Provides basic feature importance with less depth and stability. 📉
In a nutshell: Random Forest excels with superior accuracy, complex pattern recognition, and robustness, making it the preferred choice for predicting high traffic! 🌟🚀
7 | Web Page For Prediction
Recipe Predictor Web Application 🍽️¶
Welcome to the Recipe Predictor Web Application! This platform is designed to offer you a seamless experience in discovering recipes based on various inputs. Let’s explore the features and functionality of each page of the application.
📄 Description Page¶
What is Description Page?¶
The Description Page provides an overview of the web application. It introduces the core functionalities and features of the Recipe Predictor, giving users an understanding of what the app can do. This page sets the stage for a smooth user experience by:
- Explaining the Purpose: Outlines the aim of the application—to predict recipe categories based on user input.
- Showcasing Key Features: Highlights the main functionalities like recipe prediction and user-friendly design.
- Providing Technology Insights: Briefly mentions the technologies used, such as Joblib, Flask, and Random Forest.
🔑 Login Page¶
What is Login Page?¶
The Login Page is the entry point for users to access the Recipe Predictor’s features. This page ensures that user data is secure and provides personalized experiences. Key aspects include:
- User Authentication: Allows users to log in with their credentials, ensuring that their data and preferences are kept private.
- Registration Option: Provides a way for new users to sign up and create an account.
- Password Management: Includes options for users to recover or reset their passwords if needed.
🔮 Prediction Page¶
What is Prediction Page?¶
The Prediction Page is where the magic happens! Users can input their recipe details and receive predictions about the best recipe categories based on their inputs. Features of this page include:
- Input Form: Users can enter details such as recipe name, calories, carbohydrate content, sugar level, protein amount, category, and servings.
- Real-Time Prediction: After submission, the page communicates with the Random Forest model to generate and display predictions quickly.
- Interactive Results: Shows predicted categories in an easy-to-understand format, helping users make informed decisions about their recipes.
🛠️ Built With¶
- Joblib: Utilized for efficient serialization and deserialization of the Random Forest model, ensuring fast and reliable predictions.
- Flask: Serves as the web framework, handling server requests and API interactions seamlessly.
- Random Forest: The machine learning model used to classify recipes based on input features.
📄 Description Page¶
🔑 Login Page¶
🔮 Prediction Page¶
🎥 Prediction of Recipe¶
✅ When you have finished...¶
- Publish your Workspace using the option on the left
- Check the published version of your report:
- Can you see everything you want us to grade?
- Are all the graphics visible?
- Review the grading rubric. Have you included everything that will be graded?
- Head back to the Certification Dashboard to submit your practical exam report and record your presentation